一种基于云计算的跨境电商物流成本智能预测方法,Advances in Mathematical Physics

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一种基于云计算的跨境电商物流成本智能预测方法
Advances in Mathematical Physics ( IF 1.0 ) Pub Date : 2022-03-29 , DOI: 10.1155/2022/3838293
Yanting Li ₁

Affiliation

针对目前跨境电商物流成本预测方法存在预测效果差、准确率低、效率低等问题，提出一种基于云计算的跨境电商物流成本预测智能方法。分析云计算概念、特点和服务模型，研究云计算相关技术，根据BP神经网络原理训练BP神经网络算法。BP神经网络结构是通过确定输入层的神经元个数、隐藏层的神经元个数、输出层的神经元个数以及神经网络的激活函数得到的。对输入层的输入数据样本进行归一化，选择初始权重、阈值、和 BP 神经网络的学习率参数来确定动量系数。本文采用神经网络模型结合Spark云计算平台实现跨境电商物流成本的智能预测。该方法具有良好的预测能力。经过大量的数据输入输出关系训练，得到了最适合预测的模型。实验结果表明，所提方法的跨境电商物流成本预测效果良好，能够有效提高跨境电商物流成本预测的准确性和效率。该方法具有良好的预测能力。经过大量的数据输入输出关系训练，得到了最适合预测的模型。实验结果表明，所提方法的跨境电商物流成本预测效果良好，能够有效提高跨境电商物流成本预测的准确性和效率。该方法具有良好的预测能力。经过大量的数据输入输出关系训练，得到了最适合预测的模型。实验结果表明，所提方法的跨境电商物流成本预测效果良好，能够有效提高跨境电商物流成本预测的准确性和效率。

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A Cloud Computing-Based Intelligent Forecasting Method for Cross-Border E-Commerce Logistics Costs

Aiming at the problems of poor forecasting effect and low accuracy and low efficiency in current cross-border e-commerce logistics cost prediction methods, a cloud computing-based intelligent method for cross-border e-commerce logistics cost prediction is proposed. Analyze cloud computing concepts, characteristics, and service models, study cloud computing-related technologies, and train BP neural network algorithms based on BP neural network principles. The BP neural network structure is obtained by determining the number of neurons in the input layer, the number of neurons in the hidden layer, the number of neurons in the output layer, and the activation function of the neural network. Normalize the input data samples of the input layer, and select the initial weight, threshold, and learning rate parameters of the BP neural network to determine the momentum coefficient. This paper uses neural network model combined with Spark cloud computing platform to realize the intelligent prediction of cross-border e-commerce logistics cost. This method has good predictive ability. After a large amount of data input and output relationship training, it has obtained the most suitable model for prediction. The experimental results show that the cross-border e-commerce logistics cost prediction effect of the proposed method is good, and it can effectively improve the accuracy and efficiency of cross-border e-commerce logistics cost prediction.

更新日期：2022-03-29

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