冷链物流配送大数据实时监控优化仿真

Cold Chain Logistics Distribution Big Data Real-Time Monitoring Optimization Simulation

为了监控冷链物流在配送过程中产生的大数据,需要对冷链物流配送大数据实时监控方法进行优化。采用当前冷链物流配送大数据实时监控方法对冷链物流配送过程中的大数据进行实时监控时,得到的动态特征可信度低、目标定位结果精准度低。提出一种冷链物流配送大数据实时监控优化方法。结合Odds特征算法和BNS特征算法,融合差异率和BNS特征值的方差及权值得到冷链物流配送大数据的动态特征。根据动态特征得到相似性函数的最大位置,通过Bhattacharyya系数确定自适应加权和特征选择,完成冷链物流配送过程中目标数据的定位,对冷链物流配送大数据的目标数据进行实时跟踪,完成冷链物流配送大数据的实时监控。仿真结果表明,所提方法得到的动态特征可信度高、目标定位结果精准度高。

In order to monitor the big data of cold chain logistics during the distribution process,it is necessary to optimize the real-time monitoring method.Therefore,a method of real-time monitoring and optimization for big data in cold chain logistics distribution was proposed.At first,this research combined Odds feature algorithm with BNS feature algorithm and integrated variance and weight of the difference rate and the BNS feature value to obtain the dynamic feature of big data during the cold-chain logistics distribution.Then,the research obtained the maximum position of similarity function based on the dynamic feature.Meanwhile,our research used the Bhattacharyya coefficient to determine the adaptive weighting and the feature selection,so as to complete the positioning of target data during the cold-chain logistics distribution.Finally,we tracked the target data of bid data in cold chain logistics distribution in real time.Thus,we completed the real-time monitoring for big data during the cold chain logistics distribution.Simulation results prove that the proposed method has high reliability of dynamic feature and high accuracy of target positioning result.