Logistics scheduling optimisation and allocation of intercultural communication trade under internet of things and edge computing

This exploration aims to expand the functions of the traditional logistics management system based on the Internet of Things (IoT), continuously shorten the scheduling time and reduce the omission rate of data transmission. First, a logistics scheduling platform based on the IoT is designed. The platform integrates photography, positioning, laser scanning, scheduling and other functions. Then, a real-time data transmission scheduling model and optimisation algorithm based on IoT, e-commerce and deep reinforcement learning are proposed. They can improve the efficiency of data scheduling and processing on the platform, and ensure data integrity. Finally, the model is tested and evaluated. The results show that the scheduling time of the designed model is controlled within 10 s. The model scheduling data integrity rate is 91% on average. The Long Short-Term Memory model's prediction accuracy is higher than that of other models. The optimisation algorithm designed can significantly reduce the cost and time of IoT edge computing. This exploration provides technical support for cross-cultural communication and reasonable scheduling and allocation in trade.