罗非鱼冷链物流中的异常温度检测方法研究

发布时间：2018-07-13 17:02

【摘要】：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。
[Abstract]:Under the influence of cold chain logistics system and equipment, the temperature in cold chain logistics is likely to be abnormal, resulting in the deterioration of tilapia. The current abnormal temperature detection method is not suitable for practical application because of its low accuracy and performance. In this paper, a new method of detecting abnormal temperature in tilapia cold chain logistics is proposed, the criterion of abnormal temperature data in tilapia cold chain logistics is given, and the structure of fuzzy ARHMM is introduced. Whether the temperature data of tilapia cold chain logistics is abnormal or not is determined from three different aspects through the intermediate calculation results. The correct operation data set of each part is regarded as a membership function to describe the membership degree of the temperature data to be detected in the set. Through the combination of the three detection sets to determine the final membership, thus realizing the abnormal temperature detection in tilapia cold chain logistics. The experimental results show that the proposed method is used to detect the abnormal temperature in the cold chain logistics of tilapia, and the detection accuracy and overall performance are higher.
【作者单位】：广州工商学院物流系;
【基金】：广东省民办教育专项资金物流管理专业建设粤财教【2013】189号资助
【分类号】：TS254.7

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