Cloud-based configurable data stream processing architecture in rural economic development.

ABSTRACT:

Purpose

This study aims to address the limitations of traditional data processing methods in predicting agricultural product prices, which is essential for advancing rural informatization to enhance agricultural efficiency and support rural economic growth.

Methodology

The RL-CNN-GRU framework combines reinforcement learning (RL), convolutional neural network (CNN), and gated recurrent unit (GRU) to improve agricultural price predictions using multidimensional time series data, including historical prices, weather, soil conditions, and other influencing factors. Initially, the model employs a 1D-CNN for feature extraction, followed by GRUs to capture temporal patterns in the data. Reinforcement learning further optimizes the model, enhancing the analysis and accuracy of mult

SUBMITTER: Chen H 

PROVIDER: S-EPMC11623176 | biostudies-literature | 2024

REPOSITORIES: biostudies-literature