Exploration on the Construction of Cross-Border E-Commerce Logistics System Using Deep Learning
Mathematical Problems in Engineering
; 2022, 2022.
Article
in English
| ProQuest Central | ID: covidwho-1909884
ABSTRACT
The intention of this article is to solve the disadvantages of the current logistics model and promote the healthy development of modern cross-border e-commerce (CBEC) Logistics. First, this paper expounds on and compares the traditional and CBEC logistics models. Then, the CBEC logistics system is constructed and adjusted according to system construction requirements. Further, two key subsystems are designed the logistics object distribution subsystem and risk detection subsystem, based on the deep learning backpropagation neural network (BPNN) algorithm. The relevant parameters of the object distribution subsystem are calculated and sent to the risk detection subsystem model and tested. It is concluded that the sorting completion rate before 1800 can reach 95.2%, indicating that the proposed CBEC logistic system can meet the needs of CBEC logistics enterprises. Logistics risk detection’s expected and actual outputs can fit 99%, indicating a tiny deviation. The research has certain reference significance for clarifying the logistics system and service mode of CBEC.
Engineering; Deep learning; Neural networks; International; Artificial neural networks; Cost reduction; Epidemics; Back propagation networks; Consumption; Algorithms; Supply chains; International organizations; Electronic commerce; Enterprise resource planning; Subsystems; Logistics; Machine learning; Coronaviruses; Information technology; Risk; Back propagation; COVID-19
Full text:
Available
Collection:
Databases of international organizations
Database:
ProQuest Central
Type of study:
Randomized controlled trials
Language:
English
Journal:
Mathematical Problems in Engineering
Year:
2022
Document Type:
Article
Similar
MEDLINE
...
LILACS
LIS