Blockchain-Based Neural Network Model for Agricultural Product Cold Chain Coordination.

Area of Science:

• Agricultural Science
• Computer Science
• Data Science

Background:

• Agricultural cold chains generate vast amounts of small, diverse data files.
• Efficient data storage and analysis are crucial for optimizing cold chain operations.
• Existing systems struggle with the demands of large-scale agricultural data management.

Purpose of the Study:

• To develop an optimized data storage and analysis framework for agricultural cold chain coordination.
• To improve the efficiency, resource utilization, and profitability of cold chain logistics.
• To identify optimal locations for cold storage facilities using advanced algorithms.

Main Methods:

• Implemented a blockchain fusion neural network algorithm for cold chain coordination.
• Developed an improved balanced merging and index caching strategy for HDFS to handle small files.
• Utilized an improved spectral clustering algorithm for data analysis and site selection.
• Analyzed two coordination market demand forecasting models.

Main Results:

• Significantly improved HDFS performance in storing and reading numerous small files.
• Reduced error rates and processing time in data clustering.
• Successfully identified an optimal city for a large cold storage transit station.
• Validated coordination market prediction models against real-world data.

Conclusions:

• The proposed blockchain-based approach enhances agricultural cold chain data management and efficiency.
• The improved spectral clustering algorithm is effective for data analysis and strategic decision-making.
• The study provides valuable insights for optimizing cold chain logistics and maximizing enterprise profits.