A method and system for detecting the amount of emitted particulate matter for unconventional

Abstract

This invention discloses a method and system for detecting the number of unconventional particulate matter emissions, belonging to the technical field of vehicle monitoring and management. It includes the following steps: a vehicle monitoring system is installed on each heavy-duty diesel vehicle, and the vehicle monitoring system is connected to a cloud server; the vehicle monitoring system monitors the location information and pollutant emissions of the heavy-duty diesel vehicle; the cloud server generates a vehicle driving route map based on the location information and calculates the pollutant emissions; a neural network model is trained using the vehicle driving route map and pollutant emissions; when the vehicle monitoring system receives a new vehicle driving route map, it uses the neural network model to calculate the pollutant emissions corresponding to each short-distance road segment and calculates the total pollutant emissions of the new vehicle driving route map, marking the new vehicle driving route map. This invention has the effect of predicting and displaying details of pollutant emissions from heavy-duty diesel vehicles before the start of a trip, allowing vehicle owners to manage their trips in advance.

Description

Technical Field

[0001] This invention relates to the field of vehicle monitoring and management, and in particular to a method and system for detecting the number of unconventional particulate matter emissions. Background Technology

[0002] Currently, heavy-duty diesel vehicles are the main source of unconventional particulate matter emissions. Heavy-duty diesel vehicles typically refer to diesel-powered vehicles with a maximum gross vehicle weight exceeding 3.5 tons. These vehicles are widely used in logistics, engineering construction, and urban public services, making them a key target for air pollution control. Although heavy-duty diesel vehicles account for only about 10% of the total number of motor vehicles, they account for over 60% of nitrogen oxide (NOx) emissions and over 80% of particulate matter (PM) emissions. Currently, it is common practice in China to install OBD remote monitoring terminals on key operating vehicles meeting China IV and China V emission standards to monitor various vehicle data and assess pollutant emissions. Regulating heavy-duty diesel vehicles can ensure their emissions meet standards throughout their entire lifecycle.

[0003] The existing technical solutions mentioned above have the following drawbacks: Currently, the regulation of heavy-duty diesel vehicles can only be carried out after the trip is completed, and vehicle owners do not know how much pollutant will be generated or whether the emission standards will be exceeded before the trip, making it difficult for vehicle owners to systematically manage the use of heavy-duty diesel vehicles. Summary of the Invention

[0004] In order to obtain information on pollutant emissions generated by heavy-duty diesel vehicles during their journeys in advance for systematic management, this application provides a method and system for detecting the number of unconventional particulate matter emissions.

[0005] On the one hand, the technical solution provided in this application for detecting the number of unconventional particulate matter emissions is as follows: A method for detecting particulate matter number in unconventional emissions, comprising the following steps: Each heavy-duty diesel truck is equipped with a vehicle monitoring system, and all vehicle monitoring systems are wirelessly connected to a cloud server. The vehicle monitoring system monitors the location information and pollutant emissions of heavy-duty diesel vehicles and sends the location information and pollutant emissions to the cloud server. The cloud server generates a vehicle driving route map based on location information and calculates the driving speed of heavy-duty diesel vehicles based on the time the location information is received. On the vehicle route map, the route segments are divided according to the driving speed, and the route segments are colored according to the speed. The total pollutant emissions for each travel route segment are calculated based on the pollutant emissions, and the average pollutant emissions per unit time for each travel route segment are calculated using the total pollutant emissions and the corresponding travel time. The driving speed and average pollutant emissions per unit time are correlated for each road segment, and the correlated data is used to train a neural network model. When the vehicle monitoring system receives a new vehicle driving route map, it sends the new vehicle driving route map to the cloud server. The cloud server obtains the traffic conditions of the new vehicle route map, estimates the vehicle speed of each segment in the new vehicle route map based on the traffic conditions, calculates the distance traveled per unit time based on the vehicle speed of each segment, and divides the new vehicle route map into multiple short-distance segments according to the unit time. The vehicle speed of each short-distance segment in the newly calculated vehicle route map is imported into the neural network model. The neural network model calculates the pollutant emissions corresponding to each short-distance segment and calculates the total pollutant emissions of the new vehicle route map, which are then marked on the new vehicle route map.

[0006] By adopting the above scheme, the system will continuously collect information during the driving of heavy-duty diesel vehicles, train and update the neural network model in real time for heavy-duty diesel vehicles, and when the vehicle owner wants to plan a new trip, he / she can upload the route map of the trip. The system will use the neural network model to predict the pollutant emissions of each section of the route and the total pollutant emissions, so that the vehicle owner can manage the vehicle trip in advance.

[0007] Preferably, the step of "obtaining road conditions from the cloud server for the new vehicle driving route map" includes the following steps: Establish an interface between the cloud server and the map software; The cloud server uses map software to obtain traffic speed and traffic light information at various points along the new vehicle route map. The traffic conditions for each road segment are generated by combining the traffic speed and traffic light information at various points on the vehicle's route map.

[0008] By adopting the above scheme, the system can predict the traffic speed and traffic light information of vehicle routes in real time through other map software, and assess road conditions based on the traffic speed and traffic light information.

[0009] Preferably, the following steps are also included: The cloud server sets the vehicle's total pollutant emissions throughout its lifecycle. Use heavy-duty diesel vehicle information to establish vehicle life records; The pollutant emissions uploaded by the vehicle monitoring system are stored according to the time, and the total amount of pollutant emissions from the vehicle is obtained by adding up all the pollutant emissions. The remaining pollutant emissions of a vehicle are obtained by subtracting the vehicle's total pollutant emissions from its total lifetime pollutant emissions, and then sent to the vehicle monitoring system. The vehicle monitoring system displays the vehicle's remaining pollutant emissions.

[0010] By adopting the above solution, the cloud server sets up a vehicle life record for each heavy-duty diesel vehicle. The vehicle life record stores the pollutant emission records of the heavy-duty diesel vehicle, as well as the total amount of pollutants emitted, making it convenient for vehicle owners to understand the vehicle's usage.

[0011] Preferably, the following steps are also included: When the calculated remaining pollutant emissions of the vehicle are less than 0, cancel the currently added vehicle driving route map and the associated total pollutant emissions and pollutant emissions corresponding to the short-distance road segment, and send an alarm to the vehicle monitoring system. Calculate the average total pollutant emissions corresponding to all vehicle driving routes within a single vehicle life record. The estimated pollutant emissions of a vehicle are calculated by adding the vehicle's actual pollutant emissions to its average emissions. The estimated residual pollutant emissions are obtained by subtracting the total pollutant emissions from the total pollutant emissions of the vehicle throughout its life cycle. If the estimated remaining pollutant emissions are less than 0, an alarm is sent to the vehicle monitoring system.

[0012] By adopting the above scheme, when a vehicle owner plans a new trip, the system will automatically assess whether the trip will cause the total pollutant emissions of the heavy-duty diesel vehicle to exceed the standard. If it does, the trip will be canceled. The system will also remind the vehicle owner when the remaining amount of pollutants that can be emitted is insufficient, allowing the vehicle owner to plan the next steps for the heavy-duty diesel vehicle in advance.

[0013] Preferably, the following steps are also included: The cloud server is configured with the location information for recycling sites. If the remaining pollutant emissions of the heavy-duty tanker truck are less than 0 or the estimated remaining pollutant emissions are less than 0, then the current location information of the heavy-duty tanker truck is obtained, and the location information of the nearest recycling station is found based on the location information. A route map is generated starting from the current location and ending at the recycling station location, and then sent to the vehicle monitoring system. The vehicle monitoring system displays a route map.

[0014] By adopting the above scheme, if the remaining pollutants emitted by heavy-duty diesel vehicles are insufficient, the system will also actively search for the nearest recycling station and plan a route to the recycling station.

[0015] On the other hand, the technical solution provided in this application for a particulate matter number detection system for unconventional emissions is as follows: A particulate matter number detection system for unconventional emissions includes a vehicle monitoring system installed on each heavy-duty diesel vehicle and a cloud server connecting all vehicle monitoring systems. The vehicle monitoring system includes an information acquisition module, a route receiving module, and a wireless transmission module. The information acquisition module monitors the location information and pollutant emissions of heavy-duty diesel vehicles and sends them to the wireless transmission module. The route receiving module receives new travel route segments and transmits them to the wireless transmission module; The wireless transmission module sends location information, pollutant emissions, and route maps to the cloud server. The cloud server includes a route generation module, a data calculation module, a model training module, a route processing module, and a pollution prediction module; The route generation module receives location information sent by the wireless transmission module, generates a vehicle driving route map based on the location information, and sends the vehicle driving route map to the data calculation module. The data calculation module receives pollutant emissions from the wireless transmission module, calculates the speed of the heavy-duty diesel vehicle based on the time of receiving the location information, divides the driving route into segments on the vehicle driving route map according to the driving speed, colors the driving route segments according to the speed, calculates the total pollutant emissions of each driving route segment based on the pollutant emissions, and calculates the average pollutant emissions per unit time of each driving route segment by using the total pollutant emissions and the driving time corresponding to the driving route segment. The driving speed corresponding to each driving route segment and the average pollutant emissions per unit time are correlated, and the correlated data is sent to the model training module. The model training module has a preset neural network model. The neural network model is trained using the correlated data and then sent to the pollution prediction module. The route processing module receives the driving route segment sent by the wireless transmission module, obtains the road conditions of the new driving route segment, estimates the vehicle speed of each segment in the new driving route segment based on the road conditions, calculates the distance per unit time based on the vehicle speed of each segment, divides the new vehicle driving route map into multiple short-distance segments according to the unit time, and sends the divided vehicle driving route map to the pollution prediction module. The pollution prediction module imports the vehicle speed of each short-distance segment in the calculated new vehicle driving route map into the neural network model. The neural network model calculates the pollutant emissions corresponding to each short-distance segment and calculates the total pollutant emissions of the new vehicle driving route map, and marks it on the new vehicle driving route map.

[0016] By adopting the above scheme, the system will continuously collect information during the driving of heavy-duty diesel vehicles, train and update the neural network model in real time for heavy-duty diesel vehicles, and when the vehicle owner wants to plan a new trip, he / she can upload the route map of the trip. The system will use the neural network model to predict the pollutant emissions of each section of the route and the total pollutant emissions, so that the vehicle owner can manage the vehicle trip in advance.

[0017] Preferably, the route processing module has a pre-defined interface with the map software. Based on the new vehicle route map, it obtains the traffic speed and traffic light information at various points on the vehicle route map through the map software, and generates the road conditions for each road segment by combining the traffic speed and traffic light information at various points on the vehicle route map.

[0018] By adopting the above scheme, the system can predict the traffic speed and traffic light information of vehicle routes in real time through other map software, and assess road conditions based on the traffic speed and traffic light information.

[0019] Preferably, the cloud server further includes an archive management module and an archive monitoring module; The file management module presets the total pollutant emissions of a vehicle throughout its life cycle, receives information from heavy-duty diesel vehicles and establishes a vehicle life record, calls up the pollutant emissions uploaded by the vehicle monitoring system, stores the pollutant emissions according to time, and adds up all the pollutant emissions to obtain the total pollutant emissions emitted by the vehicle. The file monitoring module calls the vehicle life file of the file management module, uses the total pollutant emissions of the vehicle throughout its life cycle to subtract the pollutant emissions already emitted by the vehicle to obtain the remaining pollutant emissions of the vehicle, and sends the remaining pollutant emissions to the wireless transmission module of the corresponding heavy-duty diesel vehicle. The vehicle monitoring system includes an information display module; The wireless transmission module sends the received remaining pollutant emissions to the information display module; The information display module displays the received remaining pollutant emissions.

[0020] By adopting the above solution, the cloud server sets up a vehicle life record for each heavy-duty diesel vehicle. The vehicle life record stores the pollutant emission records of the heavy-duty diesel vehicle, as well as the total amount of pollutants emitted, making it convenient for vehicle owners to understand the vehicle's usage.

[0021] Preferably, the file management module calculates the average total pollutant emissions corresponding to all vehicle driving route maps in a single vehicle life file, adds the vehicle's already emitted pollutant emissions to the average emissions to calculate the vehicle's estimated pollutant emissions, and transmits the vehicle's estimated pollutant emissions to the file monitoring module. When the calculated vehicle residual pollutant emissions are less than 0, the archive monitoring module transmits a cancellation command to the pollution prediction module and sends an alarm to the wireless transmission module of the corresponding heavy-duty diesel vehicle. The archive monitoring module uses the vehicle's total life-cycle pollutant emissions to subtract the vehicle's total life-cycle pollutant emissions to obtain the estimated residual pollutant emissions. If the estimated residual pollutant emissions are less than 0, an alarm is sent to the wireless transmission module of the corresponding heavy-duty diesel vehicle. The wireless transmission module sends the received alarm to the information display module; The information display module displays the alarm.

[0022] By adopting the above scheme, when a vehicle owner plans a new trip, the system will automatically assess whether the trip will cause the total pollutant emissions of the heavy-duty diesel vehicle to exceed the standard. If it does, the trip will be canceled. The system will also remind the vehicle owner when the remaining amount of pollutants that can be emitted is insufficient, allowing the vehicle owner to plan the next steps for the heavy-duty diesel vehicle in advance.

[0023] Preferably, when the archive monitoring module sends an alarm to the wireless transmission module of the corresponding heavy-duty diesel vehicle, it sends the corresponding heavy-duty diesel vehicle information to the route generation module. The route generation module is pre-set with recycling station location information. When it receives heavy-duty diesel vehicle information sent by the route generation module, it obtains the current location information of the heavy-duty tanker truck, finds the nearest recycling station location information based on the location information, generates a route map with the current location information as the starting point and the recycling station location as the ending point, and sends the route map to the archive monitoring module. The archive monitoring module adds the route map to the alarm and sends it to the wireless transmission module of the corresponding heavy-duty diesel vehicle. The information display module simultaneously displays the route map within the alarm when displaying the alarm.

[0024] By adopting the above scheme, if the remaining pollutants emitted by heavy-duty diesel vehicles are insufficient, the system will also actively search for the nearest recycling station and plan a route to the recycling station.

[0025] In summary, the present invention has the following beneficial effects: 1. The system uses a neural network model to predict pollutant emissions at each segment of the journey and the total pollutant emissions, making it easier for car owners to manage their vehicle trips in advance. Attached Figure Description

[0026] Figure 1 This is an overall system block diagram of Embodiment 2 of this application.

[0027] Figure 2 This is a block diagram of the vehicle monitoring system and cloud server according to Embodiment 2 of this application. Explanation of reference numerals in the attached figures: 1. Vehicle monitoring system; 11. Information acquisition module; 12. Route receiving module; 13. Wireless transmission module; 14. Information display module; 2. Cloud server; 21. Route generation module; 22. Data calculation module; 23. Model training module; 24. Route processing module; 25. Pollution prediction module; 26. File management module; 27. File monitoring module. Detailed Implementation

[0028] Example 1: This application discloses a method for detecting the number of unconventional particulate matter emissions, with the following specific steps: Each heavy-duty diesel vehicle is equipped with a vehicle monitoring system 1, and all vehicle monitoring systems 1 are wirelessly connected to a cloud server 2.

[0029] Cloud server 2 sets the vehicle's total pollutant emissions and recycling station location information throughout its life cycle.

[0030] The vehicle monitoring system 1 monitors the location information and pollutant emissions of heavy-duty diesel vehicles and sends the location information and pollutant emissions to the cloud server 2.

[0031] The cloud server 2 generates a vehicle driving route map based on the location information and calculates the driving speed of the heavy-duty diesel vehicle based on the time of receiving the location information.

[0032] The driving route map is divided into segments based on driving speed, and the segments are colored according to the driving speed.

[0033] The total pollutant emissions for each travel route segment are calculated based on the pollutant emissions, and the average pollutant emissions per unit time for each travel route segment are calculated using the total pollutant emissions and the corresponding travel time.

[0034] The driving speed and average pollutant emissions per unit time for each road segment are correlated, and the correlated data is used to train a neural network model.

[0035] When the vehicle monitoring system 1 receives a new vehicle driving route map, it sends the new vehicle driving route map to the cloud server 2.

[0036] Establish an interface between cloud server 2 and the map software.

[0037] Cloud server 2 obtains traffic speed and traffic light information at various points along the new vehicle route map using map software.

[0038] The system combines traffic speeds and traffic light information at various points on the vehicle route map to generate road conditions for each road segment. Based on these road conditions, the system estimates the vehicle speeds on each segment of the new route map, calculates the distance traveled per unit time based on these speeds, and then divides the new route map into multiple short-distance segments according to the unit time.

[0039] The vehicle speed of each short-distance segment in the newly calculated vehicle route map is imported into the neural network model. The neural network model calculates the pollutant emissions corresponding to each short-distance segment and calculates the total pollutant emissions of the new vehicle route map, which are then marked on the new vehicle route map.

[0040] Cloud server 2 uses information from heavy-duty diesel vehicles to create vehicle life records.

[0041] The pollutant emissions uploaded by the vehicle monitoring system 1 are stored according to the time, and the total amount of pollutant emissions from the vehicle is obtained by adding up all the pollutant emissions.

[0042] The remaining pollutant emissions of a vehicle are obtained by subtracting the vehicle's already emitted pollutant emissions from the total pollutant emissions over the vehicle's entire life cycle, and then sent to the vehicle monitoring system 1.

[0043] Vehicle monitoring system 1 displays the remaining pollutant emissions of the vehicle. Cloud server 2 sets up a vehicle life record for each heavy-duty diesel vehicle, which stores the pollutant emission records of the heavy-duty diesel vehicle and the total amount of pollutants emitted, making it convenient for vehicle owners to understand the vehicle's usage status.

[0044] When the calculated remaining vehicle pollutant emissions are less than 0, cancel the currently added vehicle driving route map and the associated total pollutant emissions and pollutant emissions corresponding to short-distance road segments, and send an alarm to vehicle monitoring system 1.

[0045] Cloud server 2 calculates the average total pollutant emissions corresponding to the driving routes of all vehicles within a single vehicle life record.

[0046] The estimated pollutant emissions of a vehicle are calculated by adding the vehicle's actual pollutant emissions to its average emissions.

[0047] The estimated remaining pollutant emissions are obtained by subtracting the total vehicle lifecycle pollutant emissions from the total vehicle lifecycle pollutant emissions.

[0048] If the estimated remaining pollutant emissions are less than 0, an alarm will be issued to vehicle monitoring system 1. When the vehicle owner plans a new trip, the system will automatically assess whether the trip will cause the total pollutant emissions of the heavy-duty diesel vehicle to exceed the standard. If it does, the trip will be canceled. The system will also remind the vehicle owner when the remaining allowable pollutant emissions are insufficient, allowing the owner to plan the next steps for the heavy-duty diesel vehicle in advance.

[0049] If the remaining pollutant emissions of the heavy-duty tanker truck are less than 0 or the estimated remaining pollutant emissions are less than 0, then the current location information of the heavy-duty tanker truck is obtained, and the location information of the nearest recycling station is found based on the location information.

[0050] A route map is generated starting from the current location information and ending at the recycling station location, and then sent to the vehicle monitoring system 1.

[0051] The vehicle monitoring system 1 displays a route map. If the remaining emissions of heavy-duty diesel vehicles are insufficient, the system will also proactively locate the nearest recycling station and plan a route to the station.

[0052] The implementation principle of a method and system for detecting the number of unconventional particulate matter emissions in this application embodiment is as follows: When a heavy-duty diesel vehicle is in motion, the system continuously collects information, trains and updates the neural network model in real time for the heavy-duty diesel vehicle, and when the vehicle owner plans a new trip, he / she can upload the route map of the trip. The system predicts the pollutant emissions of each segment of the route and the total pollutant emissions through the neural network model, which makes it convenient for the vehicle owner to manage the vehicle's trip in advance.

[0053] Example 2: This application discloses a system for detecting the number of unconventional particulate matter emissions, such as... Figure 1 and Figure 2 As shown, the system includes a vehicle monitoring system 1 installed on each heavy-duty diesel vehicle and a cloud server 2 connecting all vehicle monitoring systems 1. The vehicle monitoring system 1 includes an information acquisition module 11, a route receiving module 12, a wireless transmission module 13, and an information display module 14. The cloud server 2 includes a route generation module 21, a data calculation module 22, a model training module 23, a route processing module 24, a pollution prediction module 25, a file management module 26, and a file monitoring module 27.

[0054] like Figure 2 As shown, the information acquisition module 11 monitors the location information and pollutant emissions of the heavy-duty diesel vehicle and sends them to the wireless transmission module 13. The route receiving module 12 receives new driving route segments and sends them to the wireless transmission module 13. The wireless transmission module 13 sends the location information, pollutant emissions, and route map to the cloud server 2.

[0055] like Figure 2 As shown, the route generation module 21 receives the location information sent by the wireless transmission module 13, generates a vehicle driving route map based on the location information, and sends the vehicle driving route map to the data calculation module 22.

[0056] like Figure 2As shown, the data calculation module 22 receives the pollutant emission data sent by the wireless transmission module 13, calculates the driving speed of the heavy-duty diesel vehicle based on the time of receiving the location information, divides the driving route into segments based on the driving speed on the vehicle driving route map, colors the driving route segments according to the speed, calculates the total pollutant emission of each driving route segment based on the pollutant emission data, and calculates the average pollutant emission per unit time of each driving route segment by using the total pollutant emission data and the driving time corresponding to the driving route segment. The driving speed corresponding to each driving route segment and the average pollutant emission per unit time are associated, and the associated data is sent to the model training module 23.

[0057] like Figure 2 As shown, the model training module 23 has a pre-set neural network model. It trains the neural network model using the correlated data and sends the trained model to the pollution prediction module 25. The route processing module 24 receives the driving route segments sent by the wireless transmission module 13. The route processing module 24 has a pre-set interface with map software. Based on the new vehicle driving route map, it obtains the traffic speed and traffic light information at various points on the vehicle driving route map through the map software. It then generates the road conditions for each road segment by combining the traffic speed and traffic light information. Based on the road conditions, it estimates the vehicle speed in each segment of the new driving route, calculates the distance traveled per unit time based on the vehicle speed in each segment, and divides the new vehicle driving route map into multiple short-distance segments according to the unit time. The divided vehicle driving route map is then sent to the pollution prediction module 25. The system uses other map software to predict the traffic speed and traffic light information of the vehicle driving route map in real time and evaluates the road conditions based on the traffic speed and traffic light information.

[0058] like Figure 2 As shown, the pollution prediction module 25 imports the vehicle speed of each short-distance segment in the calculated new vehicle driving route map into the neural network model. The neural network model calculates the pollutant emissions corresponding to each short-distance segment and calculates the total pollutant emissions of the new vehicle driving route map, and marks it on the new vehicle driving route map.

[0059] like Figure 2 As shown, the file management module 26 presets the total pollutant emissions of a vehicle throughout its life cycle, receives information from heavy-duty diesel vehicles and establishes a vehicle life cycle file, retrieves the pollutant emissions uploaded by the vehicle monitoring system 1, stores the pollutant emissions according to time, and adds up all pollutant emissions to obtain the total pollutant emissions emitted by the vehicle. The file management module 26 also calculates the average total pollutant emissions corresponding to all vehicle driving routes within a single vehicle life cycle file, adds the total pollutant emissions emitted by the vehicle to the average emissions to calculate the estimated pollutant emissions, and transmits the estimated pollutant emissions to the file monitoring module 27.

[0060] like Figure 2 As shown, the file monitoring module 27 calls the vehicle life file of the file management module 26, subtracts the vehicle's already emitted pollutant emissions from the total pollutant emissions over the vehicle's life cycle to obtain the vehicle's remaining pollutant emissions, and sends the remaining pollutant emissions to the wireless transmission module 13 of the corresponding heavy-duty diesel vehicle. When the calculated vehicle remaining pollutant emissions are less than 0, the file monitoring module 27 transmits a cancellation command to the pollution prediction module 25 and issues an alarm to the wireless transmission module 13 of the corresponding heavy-duty diesel vehicle. The file monitoring module 27 subtracts the vehicle's total life cycle pollutant emissions from the estimated remaining pollutant emissions, and if the estimated remaining pollutant emissions are less than 0, it issues an alarm to the wireless transmission module 13 of the corresponding heavy-duty diesel vehicle. When the file monitoring module 27 issues an alarm to the wireless transmission module 13 of the corresponding heavy-duty diesel vehicle, it sends the corresponding heavy-duty diesel vehicle information to the route generation module 21.

[0061] like Figure 2 As shown, the route generation module 21 has preset recycling station location information. When it receives the heavy-duty diesel vehicle information sent by the route generation module 21, it obtains the current location information of the heavy-duty oil tanker, finds the nearest recycling station location information based on the location information, generates a route map with the current location information as the starting point and the recycling station location as the ending point, and sends the route map to the file monitoring module 27. The file monitoring module 27 adds the route map to the alarm and sends it to the wireless transmission module 13 of the corresponding heavy-duty diesel vehicle.

[0062] like Figure 2 As shown, the wireless transmission module 13 sends the received residual pollutant emissions or alarm to the information display module 14. The information display module 14 displays the received residual pollutant emissions or alarm. When displaying an alarm, the information display module 14 simultaneously displays a route map from the alarm.

[0063] like Figure 2 As shown, cloud server 2 maintains a vehicle life record for each heavy-duty diesel vehicle. This record stores the vehicle's pollutant emission records and total emitted pollutants, allowing owners to easily monitor vehicle usage. When an owner plans a new trip, the system automatically assesses whether the trip will exceed the total pollutant emissions limit. If it does, the trip is canceled. The system also alerts the owner when the remaining allowable pollutant emissions are insufficient, allowing them to plan the vehicle's next steps. If the remaining allowable pollutant emissions are also insufficient, the system will proactively locate the nearest recycling station and plan a route to it.

[0064] The embodiments described herein are preferred embodiments of the present invention and are not intended to limit the scope of protection of the present invention. Therefore, all equivalent changes made in accordance with the structure, shape and principle of the present invention should be covered within the scope of protection of the present invention.

Claims

1. A method for detecting the number of unconventional particulate matter emissions, characterized in that, Includes the following steps: Each heavy-duty diesel vehicle is equipped with a vehicle monitoring system (1), and all vehicle monitoring systems (1) are wirelessly connected to a cloud server (2). The vehicle monitoring system (1) monitors the location information and pollutant emissions of heavy-duty diesel vehicles and sends the location information and pollutant emissions to the cloud server (2); The cloud server (2) generates a vehicle driving route map based on the location information and calculates the driving speed of the heavy-duty diesel vehicle based on the time of receiving the location information; On the vehicle route map, the route segments are divided according to the driving speed, and the route segments are colored according to the speed. The total pollutant emissions for each travel route segment are calculated based on the pollutant emissions, and the average pollutant emissions per unit time for each travel route segment are calculated using the total pollutant emissions and the corresponding travel time. The driving speed and average pollutant emissions per unit time are correlated for each road segment, and the correlated data is used to train a neural network model. When the vehicle monitoring system (1) receives a new vehicle driving route map, it sends the new vehicle driving route map to the cloud server (2). The cloud server (2) obtains the road conditions of the new vehicle driving route map, estimates the vehicle speed of each segment in the new vehicle driving route map based on the road conditions, calculates the distance per unit time based on the vehicle speed of each segment, and divides the new vehicle driving route map into multiple short-distance segments according to the unit time. The vehicle speed of each short-distance segment in the newly calculated vehicle route map is imported into the neural network model. The neural network model calculates the pollutant emissions corresponding to each short-distance segment and calculates the total pollutant emissions of the new vehicle route map, which are then marked on the new vehicle route map.

2. The method for detecting the number of unconventional particulate matter emissions according to claim 1, characterized in that, The step "cloud server (2) obtains road conditions of new vehicle driving route map" includes the following steps: Establish an interface between the cloud server (2) and the map software; The cloud server (2) obtains the traffic speed and traffic light information at various points on the new vehicle driving route map through map software; The traffic conditions for each road segment are generated by combining the traffic speed and traffic light information at various points on the vehicle's route map.

3. The method for detecting the number of unconventional particulate matter emissions according to claim 1, characterized in that, It also includes the following steps: Cloud server (2) sets the total pollutant emissions of the vehicle throughout its life cycle; Use heavy-duty diesel vehicle information to establish vehicle life records; The pollutant emissions uploaded by the vehicle monitoring system (1) are stored according to the time, and the total amount of pollutant emissions is added together to obtain the total amount of pollutant emissions emitted by the vehicle. The remaining pollutant emissions of a vehicle are obtained by subtracting the amount of pollutants already emitted by the vehicle from the total pollutant emissions of the vehicle throughout its life cycle, and then sent to the vehicle monitoring system (1). The vehicle monitoring system (1) displays the remaining pollutant emissions from the vehicle.

4. The method for detecting the number of unconventional particulate matter emissions according to claim 3, characterized in that, It also includes the following steps: When the calculated remaining pollutant emissions of the vehicle are less than 0, cancel the currently added vehicle driving route map and the associated total pollutant emissions and pollutant emissions corresponding to the short-distance road segment, and issue an alarm to the vehicle monitoring system (1); Calculate the average total pollutant emissions corresponding to all vehicle driving routes within a single vehicle life record. The estimated pollutant emissions of a vehicle are calculated by adding the vehicle's actual pollutant emissions to its average emissions. The estimated residual pollutant emissions are obtained by subtracting the total pollutant emissions from the total pollutant emissions of the vehicle throughout its life cycle. If the estimated remaining pollutant emissions are less than 0, an alarm is sent to the vehicle monitoring system (1).

5. The method for detecting the number of unconventional particulate matter emissions according to claim 4, characterized in that, It also includes the following steps: (2) Set the location information of the recycling site on the cloud server; If the remaining pollutant emissions of the heavy-duty tanker truck are less than 0 or the estimated remaining pollutant emissions are less than 0, then the current location information of the heavy-duty tanker truck is obtained, and the location information of the nearest recycling station is found based on the location information. A route map is generated starting from the current location information and ending at the location of the recycling station, and the route map is sent to the vehicle monitoring system (1); The vehicle monitoring system (1) displays a route map.

6. A system for detecting the number of unconventional particulate matter emissions, characterized in that: It includes a vehicle monitoring system (1) installed on each heavy-duty diesel vehicle and a cloud server (2) connecting all vehicle monitoring systems (1). The vehicle monitoring system (1) includes an information acquisition module (11), a route receiving module (12), and a wireless transmission module (13). The information acquisition module (11) monitors the location information and pollutant emissions of the heavy-duty diesel vehicle and sends them to the wireless transmission module (13); The route receiving module (12) receives new travel route segments and transmits them to the wireless transmission module (13); The wireless transmission module (13) sends the location information, pollutant emissions and route map to the cloud server (2); The cloud server (2) includes a route generation module (21), a data calculation module (22), a model training module (23), a route processing module (24), and a pollution prediction module (25); The route generation module (21) receives the location information sent by the wireless transmission module (13), generates a vehicle driving route map based on the location information, and sends the vehicle driving route map to the data calculation module (22). The data calculation module (22) receives the pollutant emission amount sent by the wireless transmission module (13), calculates the driving speed of the heavy-duty diesel vehicle based on the time of receiving the location information, divides the driving route segments on the vehicle driving route map according to the driving speed, colors the driving route segments according to the speed, calculates the total pollutant emission amount of each driving route segment based on the pollutant emission amount, and calculates the average pollutant emission amount per unit time of each driving route segment through the total pollutant emission amount and the driving time corresponding to the driving route segment, associates the driving speed corresponding to each driving route segment with the average pollutant emission amount per unit time, and sends the associated data to the model training module (23). The model training module (23) has a preset neural network model. It uses the correlated data to train the neural network model and sends the trained neural network model to the pollution prediction module (25). The route processing module (24) receives the driving route segment sent by the wireless transmission module (13), obtains the road conditions of the new driving route segment, estimates the vehicle speed of each segment in the new driving route segment based on the road conditions, calculates the distance per unit time based on the vehicle speed of each segment, divides the new vehicle driving route map into multiple short-distance segments according to the unit time, and sends the divided vehicle driving route map to the pollution prediction module (25). The pollution prediction module (25) imports the vehicle speed of each short-distance road segment in the calculated new vehicle driving route map into the neural network model. The neural network model calculates the pollutant emissions corresponding to each short-distance road segment and calculates the total pollutant emissions of the new vehicle driving route map, and marks it on the new vehicle driving route map.

7. The particulate matter number detection system for unconventional emissions according to claim 6, characterized in that: The route processing module (24) has a preset interface with the map software. Based on the new vehicle route map, it obtains the traffic speed and traffic light information at various points on the vehicle route map through the map software, and generates the road conditions for each road segment by combining the traffic speed and traffic light information at various points on the vehicle route map.

8. The system and method for detecting the number of unconventional particulate matter emissions according to claim 6, characterized in that: The cloud server (2) also includes an archive management module (26) and an archive monitoring module (27); The file management module (26) presets the total pollutant emissions of the vehicle throughout its life cycle, receives information on heavy-duty diesel vehicles and establishes a vehicle life file, calls up the pollutant emissions uploaded by the vehicle monitoring system (1), stores the pollutant emissions according to time, and adds up all the pollutant emissions to obtain the total pollutant emissions emitted by the vehicle. The file monitoring module (27) calls the vehicle life file of the file management module (26), uses the total pollutant emissions of the vehicle throughout its life to subtract the pollutant emissions already emitted by the vehicle to obtain the remaining pollutant emissions of the vehicle, and sends the remaining pollutant emissions to the wireless transmission module (13) of the corresponding heavy-duty diesel vehicle. The vehicle monitoring system (1) includes an information display module (14); The wireless transmission module (13) sends the received remaining pollutant emissions to the information display module (14); The information display module (14) displays the received remaining pollutant emissions.

9. The system and method for detecting the number of unconventional particulate matter emissions according to claim 8, characterized in that: The file management module (26) calculates the average total pollutant emissions corresponding to all vehicle driving route maps in a vehicle life file, adds the vehicle's already emitted pollutant emissions to the average emissions to calculate the vehicle's estimated pollutant emissions, and transmits the vehicle's estimated pollutant emissions to the file monitoring module (27). When the calculated vehicle residual pollutant emissions are less than 0, the archive monitoring module (27) transmits a cancellation command to the pollution prediction module (25) and sends an alarm to the wireless transmission module (13) of the corresponding heavy-duty diesel vehicle. The archive monitoring module (27) uses the vehicle's total life-cycle pollutant emissions to subtract the vehicle's total life-cycle pollutant emissions to obtain the estimated residual pollutant emissions. If the estimated residual pollutant emissions are less than 0, an alarm is sent to the wireless transmission module (13) of the corresponding heavy-duty diesel vehicle. The wireless transmission module (13) sends the received alarm to the information display module (14); The information display module (14) displays the alarm.

10. The system and method for detecting the number of unconventional particulate matter emissions according to claim 9, characterized in that: When the file monitoring module (27) sends an alarm to the wireless transmission module (13) of the corresponding heavy-duty diesel vehicle, it sends the corresponding heavy-duty diesel vehicle information to the route generation module (21). The route generation module (21) has preset recycling station location information. When it receives the heavy-duty diesel vehicle information sent by the route generation module (21), it obtains the current location information of the heavy-duty oil tanker, finds the nearest recycling station location information based on the location information, generates a route map with the current location information as the starting point and the recycling station location as the ending point, and sends the route map to the file monitoring module (27). The file monitoring module (27) adds the route map to the alarm and sends it to the wireless transmission module (13) of the corresponding heavy-duty diesel vehicle. The information display module (14) displays the route map in the alarm simultaneously when displaying the alarm.

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