基于LWOA-TSVR的冷藏食品冷链物流质量安全预警模型

Early-warning model for refrigerated food quality and safety of cold chain logistics based on LWOA-TSVR

冷链物流在冷藏食品质量安全监控过程中十分重要,因此,对冷链运输过程中的食品质量安全进行实时预警是有意义的。文中从智能信息处理的角度出发,通过引入小波变换,对不同的样本误差赋予不同的权重,建立了一种基于小波变换的加权SVR算法(wavelet transform based weighted SVR,WTWSVR),进而提高模型的抗噪声性能。并通过引入基于莱维飞行改进的鲸鱼算法(levy-flight whale optimization algorithm,LWOA),实现对WTWSVR算法的参数优化,提高预测效率。最后,将LWOA-WTWSVR算法应用于冷藏产品冷链运输预警系统中。实验结果表明,采用优化后的WTWSVR算法进行冷冻产品冷链运输质量安全预警具有明显的优势。

Cold chain logistics plays an important role in the quality and safety monitoring for refrigerated food.Therefore,the real-time early-warning for food quality and safety is meaningful in the process of cold chain logistics.In this paper,from the perspective of intelligent information processing,a wavelet transform based weighted SVR(WTWSVR)algorithm is established to improve the anti-noise performance of the model by introducing wavelet transform and giving different weights to different sample errors.Meanwhile,the parameters of WTWSVR algorithm is optimized based on the Levy-flight whale optimization algorithm(LWOA),which can improve the prediction efficiency.Finally,the LWOA-WTWSVR algorithm is applied to the cold chain logistics early-warning system of refrigerated products.Experiment results show that this algorithm has obvious advantages in the early-warning system.