Pollutant discharge monitoring, preventing and controlling system, method and device and medium

Abstract

The invention discloses a heavy-duty vehicle pollutant discharge monitoring, prevention and control system, method and device and a storage medium. The system comprises an edge vehicle-mounted terminal module, an edge computing node module and a prevention and control cloud computing server module, the prevention and control cloud computing server module is used for training a heavy-duty vehicle pollutant discharge prediction model and sending the trained prediction model to each roadside unit in the edge computing node module, so that each roadside unit in the edge computing node module can independently complete the prediction task of the pollutant discharge amount of the heavy vehicle, the computing time delay can be effectively reduced, and the system operation efficiency is improved;and the pollutant emission monitoring accuracy is considered, and meanwhile, the pollutant emission of the heavy vehicle can be monitored, prevented and controlled only by using the data of the vehicle-mounted OBD equipment installed on the heavy vehicle, so that the manpower and material resource cost in the implementation process is reduced, the resource waste is avoided, and the working efficiency is improved. The method is widely applied to the field of heavy-duty vehicle pollution monitoring, prevention and control.

Description

technical field [0001] The invention relates to the field of pollution monitoring, prevention and control of heavy-duty vehicles, in particular to a monitoring and prevention and control system, method, device and storage medium for pollutant discharge of heavy-duty vehicles. Background technique [0002] Motor vehicle emissions have become the primary source of PM2.5 in large and medium-sized cities, among which heavy-duty diesel vehicles are the top priority. The ownership of heavy-duty diesel vehicles accounted for less than 5%, but NO X and PM emissions accounted for as high as 52% and over 60% respectively. The actual emission of heavy-duty diesel vehicles exceeds the standard seriously, and vehicles with low environmental protection standards are sold as National IV and National V vehicles. In the practice of heavy-duty vehicle emission inspection and supervision, the inspectors felt the poor emission control effect of heavy-duty diesel vehicles; during regular annua...

AI Technical Summary

Problems solved by technology

[0006] OBD feedback data belongs to high-dimensional time-series data, and time-series data prediction models such as recurrent neural network (RNN) need to use servers with powerful computing capabilities to run in a cloud computing environment, but the current cloud computing-oriented pollution monitoring technology is difficult to directly apply In the edge computing environment composed of vehicle OBD and roadside facilities, this is because:

[0007] (1) The networked monitoring and prevention and control of heavy-duty vehicles have high requirements for real-time performance. From the OBD feedback information to the roadside unit, and then to the roadside unit to upload data to the cloud computing center, and finally the cloud computing center returns to the vehicle OBD. There will be a long delay. At this time, the vehicle may have left the target area and cannot normally receive the returned control information, causing some regulatory problems;

[0008] (2) Heavy-duty vehicles mostly drive on expressways and national highways, with remote geographical location, complex terrain and different network environments, so that OBD can smoothly feed back data to the roadside unit, and the connection between the roadside unit and the remote cloud computing center Communication requires a lot of resources, resulting in unnecessary waste;

[0009] (3) In the cloud computing mode, the calculation and control are all centralized settings. Once the cloud computing center fails, it will lead to the paralysis of the pollution monitoring of heavy vehicles on the whole road, and it will be more difficult for disaster recovery and backup.

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