罗非鱼冷链物流中的异常温度检测方法研究

受冷链物流系统和设备的影响,冷链物流中的温度很可能出现异常,导致罗非鱼变质。当前异常温度检测方法检测精度和性能低,不适于实际应用。为此,提出一种新的罗非鱼冷链物流中的异常温度检测方法,给出罗非鱼冷链物流中异常温度数据的判据,介绍了模糊ARHMM的结构。通过中间计算结果从三个不同方面确定罗非鱼冷链物流中的温度数据是否异常,把各部分正确的运算数据集看作一个隶属度函数,以此描述待检测温度数据在该集合的隶属度;通过结合得到的三个检测集确定最终隶属度,从而实现罗非鱼冷链物流中的异常温度检测。实验结果表明,采用所提方法对罗非鱼冷链物流中的异常温度进行检测,检测精度和整体性能均较高。

Tilapia cold-chain logistics abnormal temperature detection method in the study

the influence of the cold chain logistics systems and equipment, temperature of cold chain logistics is likely to be abnormal, tilapia metamorphic, the abnormal temperature low detecting precision and performance test method, not suitable for practical application.For this, put forward a new kind of tilapia cold-chain logistics of abnormal temperature detection method, abnormal temperature data in tilapia cold chain logistics is given criterion, this paper introduces the structure of fuzzy ARHMM.Through intermediate results from three different aspects to determine whether the temperature data of tilapia cold-chain logistics, correct the parts operation data set as a membership function, in order to describe the temperature data to be detected in the collection of membership degree, is obtained by combining the three test set to determine the final membership, so as to realize tilapia detecting abnormal temperature in cold chain logistics.The experimental results show that the proposed method to test the tilapia abnormal temperature of cold chain logistics, and overall performance are higher accuracy.