物流货箱温度异变信号实时挖掘技术仿真

Simulation on Real-Time Mining Technology of Temperature Variation Signals for Logistics Case

在物流货箱内温度异变信号的准确识别问题的研究中,由于集装箱内温度随着货车运输地点不同,呈现非可控性突变。RFID技术在不同区域采集的温度在非可控框架内容易发生温度偏差,造成温度信号突变。传统的温度挖掘模型对不同环境下造成的温度偏差无法做出提前预判,形成虚报警。为解决上述问题,提出基于拓扑关系搜索模型的物流货箱温度异变信号实时挖掘方法。对物流货箱温度数据进行信息重构,为温度异变信号挖掘提供依据。建立温度信号采样点拓扑关系搜索模型,完成异变信号的搜索,实现物流货箱温度异变信号的实时挖掘。实验结果表明,利用改进算法进行物流货箱温度异变信号实时挖掘,能够保证挖掘的精确度,从而为物流运输提供合理的决策依据。

Traditional temperature mining model for logistics case cannot make prediction of temperature deviation in advance, which may form a false alarm. To solve this problem, a case temperature variation signal real - time mining method is given based on the topological relationship. Through logistics case temperature data reconstruction, the temperature variation signal is obtained. A temperature signal sampling points topology search model is setup, and the temperature variation signal of logistics container is real - time searched and mined. Experimental results show that the improved algorithm can guarantee the accuracy of the excavation, so as to provide a basis for rational decision - making in logistics transportation.