| With the rapid development of my country’s economy,people’s living standards continue to improve,and the demand for material life has changed from satisfying food and clothing to enjoying.Fresh agricultural products are loved by the people because of their health and nutrition.The output of fresh agricultural products and demand is also increasing.The timeliness and perishability of fresh agricultural products make cold chain logistics an important means to ensure the quality and safety of fresh agricultural products in circulation.However,at this stage,the development of cold chain logistics in my country is still in its infancy.How to establish and improve the cold chain logistics system to meet the people’s demand for fresh agricultural products has become a hot issue.Ganzhou City is rich in agricultural resources,and the output of fresh agricultural products such as fruits and vegetables is relatively large.Because of its proximity to the Pearl River Delta Bay Area,there is a huge market demand for fresh agricultural products.The importance of cold chain logistics to the circulation of fresh agricultural products and social and economic development in Ganzhou City is obvious..The establishment of a scientific forecasting index system can better understand the nature of cold chain logistics demand,and predict the cold chain logistics demand of fresh agricultural products in Ganzhou in advance to lay the foundation for cold chain logistics planning.Combining theoretical research and empirical analysis,this thesis studies the development status and problems of cold chain logistics of fresh agricultural products in Ganzhou City,analyzes the important factors that affect the demand for cold chain logistics of fresh agricultural products in Ganzhou City,builds a GA-BP neural network prediction model,and Ganzhou will forecast and analyze the demand for cold chain logistics of fresh agricultural products in the next five years,and put forward suggestions for the development of cold chain logistics based on future demand trends.Firstly,the concept of cold chain and cold chain logistics demand for fresh agricultural products is defined,the production of fresh agricultural products is multiplied by the circulation rate of cold chain to measure the demand for cold chain logistics of fresh agricultural products,and the cold chain demand forecasting model is compared and analyzed.The advantages of the neural network forecasting model for cold chain logistics demand forecasting are expounded.The analysis of the current situation of fresh food cold chain logistics in Ganzhou City illustrates the difficulties encountered in the development of cold chain logistics.Starting from the four factors of social economy,industrial structure,logistics development and market supply and demand,the gray relational degree analysis is used to select and construct forecasting indicators,and the correlation between indicators is reduced through principal component analysis and key indicators are extracted.Aiming at the problems of poor model training and low prediction accuracy due to the random assignment of initial weights and thresholds of the BP neural network,a genetic algorithm(GA)is introduced to optimize the BP neural network,and the optimal weights and thresholds obtained by using the genetic algorithm are optimized.Instead of randomly generated weights and thresholds,the prediction accuracy of the optimized GA-BP neural network model is significantly higher than that of the BP neural network model.At the same time,the prediction results of the optimized GA-BP neural network are basically consistent with the actual situation of the cold chain logistics development of fresh agricultural products in Ganzhou City,indicating that the model established in this thesis has certain significance for the study of cold chain logistics demand forecasting.The prediction results have certain reference value for the development planning of cold chain logistics. |
