Rural Smart Logistics Management System under Multimodal Data Fusion

The purpose of this study is to explore in depth the application of multimodal data fusion technology in the rural smart logistics management system and the challenges it faces. This paper proposes a fusion model CNN-RNN that combines Convolutional Neural Network (CNN) and Convolutional Neural Network (RNN) to address the issues of data silos, limited data processing capabilities, low data fusion efficiency, and shortcomings in data security and privacy protection in current rural logistics management. The CNN-RNN model not only optimizes the data processing flow and improves the efficiency of data fusion, but also strengthens the security and privacy protection of data, laying the foundation for the progress of rural smart logistics systems. It evaluated the performance of the CNN RNN fusion model during the experimental phase. In the data processing efficiency evaluation experiment, the CNN-RNN fusion model can be used to reduce the average data processing time at different data scales. In the logistics cost optimization analysis experiment, there were reductions of 16.7%, 14.3%, and 13.3% in local distribution, regional distribution, and cross regional distribution scenarios, respectively. In the service response time research experiment, the service response time was reduced by 50%, 60%, and 50% in order processing, goods query, and delivery scheduling, respectively. From the above data conclusion, it can be seen that the CNN-RNN fusion model has achieved significant results in the field of multimodal data fusion, improving data processing efficiency, reducing logistics costs and service response time, and enhancing customer satisfaction.