The invention discloses a tire underpressure recognition method and system based on machine learning and a storage medium. The method comprises the steps: wheel speed data are obtained through a vehicle-mounted ABS; the wheel speed data under the conditions of left front wheel underpressure , right front wheel underpressure, left rear wheel underpressure, right rear wheel underpressure, two frontwheel underpressure, two rear wheel underpressure, left front wheel and right rear wheel underpressure, right front wheel and left rear wheel underpressure, only left front wheel normality, only rightfront wheel normality, only left rear wheel normality, only right rear wheel normality and four-wheel underpressure and four-wheel normality are inputted into a neural network to train the neural network; and after the training is finished, the real-time wheel speed data of the automobile are inputted into the neural network to obtain the tire pressure condition. In the automobile running process, the neural network can be trained according to the real-time tire pressure data reflected by the automobile running road condition, the driving habit of the driver, the load, the load distribution and other factors, the tire pressure monitoring processing speed can be increased, and the underpressure problem of all tires is solved.
公开了一种基于机器学习的轮胎欠压识别方法、系统及存储介质。所述方法包括:通过车载的ABS系统获取轮速数据;分别将汽车在左前轮欠压,右前轮欠压,左后轮欠压,右后轮欠压,两前轮欠压,两后轮欠压,左前轮与右后轮欠压,右前轮与左后轮欠压,仅左前轮正常,仅右前轮正常,仅左后轮正常,仅右后轮正常以及四轮欠压和四轮正常情况下的轮速数据输入神经网络,对神经网络进行训练;训练完成后,将汽车实时轮速数据输入神经网络,获得胎压情况。在汽车行驶过程中,可以根据汽车行驶的路况,驾驶员的驾驶习惯以及载重和负载分配等因素所反映出的实时胎压数据对神经网络进行训练,可以提高胎压监测的处理速度,并解决全部轮胎的欠压问题。
Tire underpressure recognition method and system based on machine learning and storage medium
基于机器学习的轮胎欠压识别方法、系统及存储介质
2020-08-04
Patent
Elektronische Ressource
Chinesisch
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