Remote vehicle data acquisition method and system

Abstract

The invention relates to a remote vehicle data acquisition method and system. The method comprises the the steps of acquiring a data acquisition demand and a data acquisition range; converting the data acquisition demand into a corresponding acquisition instruction; determining a target vehicle to acquire data according to the data acquisition range; and sending the acquisition instruction to thetarget vehicle, so as to enable the target vehicle to acquire and upload corresponding data according to the acquisition instruction. According to the scheme, the server sends an instruction to the vehicle to obtain data, and a user can select any number and type of data according to actual application requirements, so that the required data is accurately obtained, the limitation of data burying points is avoided, the application range is wide, and the expansibility is high; and flexible setup is achieved, and different types of data can be acquired from different vehicles, so that different analysis items can be conveniently carried out in batches after the data is acquired.

Description

technical field [0001] The present application relates to the technical field of Internet of Vehicles, in particular to a remote vehicle data collection method and system. Background technique [0002] With the continuous improvement of the intelligence of automobiles, automobiles have gradually evolved into an intelligent mobile data terminal, and vehicle data has been increasingly applied to vehicle status monitoring, driving behavior analysis, fault analysis, and self-learning of vehicle control algorithms. With the continuous improvement of the importance of vehicle data and the rapid development of application scenarios, the accurate collection of vehicle data is becoming more and more important. [0003] There are two main methods of automatic vehicle data collection at present: [0004] The first solution is to record and return data through the bus data recording device. The data recording device is connected to the OBD (On Board Diagnostics) interface of the vehic...

AI Technical Summary

Problems solved by technology

Considering the consumption of uploaded data traffic and the storage of background server data, this solution pre-buries less uploaded data, and the upload interval is longer, generally 30 seconds or 60 seconds

[0006] The disadvantages of the first solution are: (1) The external bus data recording equipment is expensive and cannot be used on a large scale. It is generally only used for single-vehicle fault monitoring and analysis, and is not suitable for vehicle big data analysis; (2) Vehicle network architecture It is becoming more and more complicated, and there are more and more network structures with more than 4 network segments, but the data recorder only has fixed data channels (usually only 2, if the data channel is increased, the equipment cost will increase greatly), and it is impossible to record simultaneously. Multi-data in multiple network segments; (3) can only record messages with certain IDs preset in the connected network segment, and cannot change data collection requirements in real time according to needs; (4) With the strengthening of information security , the vehicle OBD port data is gradually closed or encrypted, and the use of data recorders will become more and more restricted

[0007] The disadvantages of the second solution are: (1) The method of data burying is used to collect and upload vehicle data. In the actual application process, it is inevitable that some of the pre-buried uploaded data will not be covered, resulting in limited application (for example, if you want to count the high temperature in a certain area The tire pressure value of the vehicle under the weather, if the pre-designed data buried point does not include the tire pressure data, the statistical analysis cannot be performed); (2) The upload period of the collected and uploaded vehicle data is long, and it cannot be adjusted according to actual application requirements , not suitable for some application scenarios that require precise data such as vehicle failure analysis and driving behavior analysis (for example, if you want to analyze the driving habits of users with high fuel consumption, you must accurately capture information such as the depth and corresponding time of the driver's accelerator pedal and accelerator pedal depression , a long data collection and upload cycle will miss a lot of key information); (3) all vehicles collect and upload the same pre-embedded data, and it is impossible to analyze multiple vehicles in batches at the same time (for example, it is impossible to use a batch of vehicles for User driving habit data statistics, another batch of cars for automatic driving performance analysis data collection)

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