论文部分内容阅读
近年来对车联网的研究增多,标准化速度也明显加快。随着信息社会的发展,车载设备通信互联的需求明显增加。车载通信不同于传统通信,其传输环境时变性和空间变化都非常迅速,且受制于能源供应。本文基于神经网络模型提出了面向车联网信息的电能信息挖掘算法,可以基于大数据实现车载传感器相关信息的特征提取,并提供驾驶员优化的电能管理方案提高车辆续航时间。测试结果表明所提算法可以提取驾驶员驾驶特征并评估车辆续航能力。该算法未来可用于支持车联网的车载设备设计及智能交通系统中。
In recent years, the research on car networking has increased, and the speed of standardization has also accelerated noticeably. With the development of information society, the demand for interconnection of vehicle-mounted equipment has obviously increased. Vehicle communication is different from traditional communications, its transmission environment, the time of change and space changes are very rapid, and subject to the energy supply. Based on the neural network model, this paper proposes an energy information mining algorithm for vehicle networked information, which can extract the features of vehicle-mounted sensor based on big data and provide driver’s optimized power management scheme to improve vehicle life. The test results show that the proposed algorithm can extract driver’s driving characteristics and evaluate the vehicle’s endurance. The algorithm can be used in the future to support vehicle networking equipment design and intelligent transportation system.