Research On Logistics Demand Forecast Of Fresh Agricultural Products Based On Grey Neural Network

In a nation that prioritizes agriculture,agriculture has a significant influence on people’s lives.The production and demand of fresh agricultural products(fruits,veggies,dairy products,eggs,meat,and poultry goods are just a few examples.)are rising along with the nation’s economy’s continued growth and the enhancement of people’s quality of life The quality and safety of food are other topics that people are becoming more and more interested in.Because there are many kinds of agricultural products,and because of their own characteristics,eggs have different needs for the ecological environment.And because fresh agricultural products are not storable,perishable and metamorphic,Because there will be a lot of transit and the products will be of low value,there will be more demands on the logistical equipment for storage and transportation.Hence,powerful real-time monitoring functionality is required in transportation and storage facilities,but with effective climate regulation technology as well.In order to guarantee the quality and safety of farm goods during manufacturing and distribution,cold chain transportation is crucial.Because of this,the issue of how to establish a trustworthy cold chain logistics system to ensure the quality and safety of perishable food has progressively developed into a hot topic that is related to people’s livelihood and has an effect on the modernization and transformation of the agricultural and food sector.Thus,in order to more effectively respond to the demands of our nation’s growth of cold chain logistics for fresh agricultural goods,it is necessary to predict the demand scientifically.This research investigates the logistics demand forecast of fresh agricultural items using the grey prediction model and BP neural network.Demand forecasting for fresh agricultural items is done before introducing the related ideas of cold chain logistics,a detailed study of the research state of fresh agricultural products logistics demand forecast at home and abroad was conducted.Then,in light of the circumstances,the variables affecting the demand for fresh farm goods in Z city’s cold chain operations were outlined,and 13 quantitative indicators of this demand were built.The grey relational degree model was then used to determine the level of relationship between each one of these 13 factors and the demand for fresh agricultural products in the cold chain logistics,then select correlation is more excellent ten indicators.Then,the BP neural network prediction model,grey neural network combo prediction model,and grey prediction model are presented.Lastly,in accordance with the actual circumstances of Z city,an empirical research is conducted.First,the introduction of Z city’s fresh farm product growth state.Three projection models are applied to forecast the logistics demand for fresh agricultural goods in Z city,and the prognosis outcomes are contrasted.The model’s analysis reveals that it can quickly and accurately forecast changes in the logistics demand level for fresh agricultural goods in Z city.The model’s predicted outcomes can also serve as a guide for Z city’s future logistics planning and management.The logistics demand forecasting system proposed in this thesis has strong practicability.Ultimately,Based on the results of the projection for the expansion of fresh agricultural commodities’ cold chain logistics in Z City in the future from the standpoints of building the cold chain logistics,recommendations are given,building the transportation system,developing government policies,businesses,and industries.It also has significant reference value for the growth of fresh farm produce cold chain logistics in other parts of China.