基于向量分割的动态称重模型优化方法

发布时间：2018-05-12 15:40

  本文选题：智能运输系统 + 动态称重　； 参考：《公路交通科技》2017年07期

【摘要】：动态称重技术能够显著减少货车称重时间提高收费站通行能力,然而受其模型所限,对于变速运动或车轮静止于传感器上的车辆则无法准确进行测量。针对以上问题,以路面交互模型以及部分承载式动态称重模型为基础,提出了一种基于向量分割的部分承载动态称重模型。利用压电石英传感器结合该模型,以动态传感器稀疏阵列的形式构建了一种4排直列式动态称重系统。在哈同高速哈尔滨东收费站对系统进行了低温、定性与定量的测试。试验结果表明:该系统稳定性好,在-28℃状态下温度漂移仅为0.5‰,响应速度为0.2 s;在95%置信区间内,精确度为±3%以内,特别是能够解决动态称重中"二次起停"的问题。该系统能够为重载交通智能化管理提供一定的支持。
[Abstract]:Dynamic weighing technology can significantly reduce the loading time of freight cars and improve the capacity of toll stations. However, due to the limitation of its model, it is impossible to accurately measure vehicles with variable speed motion or stationary wheels on the sensor. Aiming at the above problems, a dynamic weighing model based on vector partitioning is proposed based on the pavement interaction model and the partial load-bearing dynamic weighing model. Based on the piezoelectric quartz sensor and the model, a 4-row linear dynamic weighing system is constructed in the form of sparse array of dynamic sensors. The low temperature, qualitative and quantitative tests of the system were carried out at Harbin East Toll Station. The test results show that the system has good stability, the temperature drift is only 0.5 鈥，

本文编号：1879176