摘要
通过对高铁车厢车载温度传感数据状态模式的准确挖掘,实现对高铁车厢温度的精确控制,提高高铁运行性能。传统方法中对车载温度传感器的数据状态模式挖掘采用的是半导体气敏传感器测量法进行数据采集,并采用神经网络控制法实现数据状态模式挖掘,方法受限于温度数据的均衡控制无法准确把握,控制效果不好。提出一种基于正态相关的高铁车载温度传感数据状态模式挖掘方法,首先进行温度传感数据采集的硬件系统设计,在原始数据采集的基础上,提取温度传感数据的正态相关状态信息特征,采用PID控制器进行列车车厢的温度调节,温度变化率、积分时间和微分时间通过线性组合的方式进行控制,得到温度传感数据的自相关控制状态方程。数据跟踪器设计成二阶离散数据跟踪器,以满足实际需要。实现了温度传感数据的状态模式挖掘改进。仿真结果表明,算法温度控制和功率控制的稳定性和准确性优越传统算法,车载温度智能调节和控制性能提高。
Accurate mining on High-speed Rail carriage vehicle temperature sensing data state model is constructed for the precise control of the High-speed Rail compartment temperature, improve the High-speed Rail performance. The traditional method of mining is used in measurement of semiconductor gas sensors for data acquisition, and the neural network control method is used to achieve the state of the data mining, balance control method is restricted, the temperature data cannot be accurately grasped, control effect is not good. A state mode mining algorithm of High-speed Rail on-board temperature sensing data is proposed based on normal correlation, temperature sensor data acquisition of original data collection is obtained, normal state information characteristic temperature sensor data is extracted to train the PID controller temperature control, temperature change rate, the integral time and differential time is controlled by a linear combination of the way, the autocorrelation control equation of state temperature sensing data is got. Data tracker is designed into two order discrete data tracker, to meet the actual needs. The mode of temperature sensing data mining is improved. The simulation results show that the stability and accuracy is better than the traditional algorithm in temperature control algorithm and power control, intelligent vehicle temperature regulation and control performance is improved.
出处
《科技通报》
北大核心
2015年第4期239-241,共3页
Bulletin of Science and Technology
关键词
正态相关
高铁车厢
温度传感器
数据挖掘
normal correlation
high-speed rail compartment
temperature sensor
data mining
