Research On High-speed Rail Meal Sales Forecast Management System Based On Neural Network

With the rapid development of high-speed railways,more and more passengers choose high-speed rail as a mode of transportation.In the increasingly competitive market,the high-speed railway passenger transport market has gradually changed from“seller market” to “buyer market”.How to attract passengers efficiently,improve service quality,and increase corporate profits.However,due to the short-term shelf life and the limited speciality of the replenishment site,the cold-chain catering sold on the high-speed rail has caused the shortage of stocks to be in short supply,overstocking and waste of resources.Therefore,the accurate forecast for high-speed rail meal sales marketing is to improve the economy.Benefits have important research implications.Since the use of the 12306 ticket system,a large number of passenger ticket purchase data has been accumulated,but these data have not been fully utilized in passenger transportation organization and marketing,and have not directly contributed to the improvement of operating income.In this paper,the neural network model is constructed by using TensorFlow framework.The high-speed rail ticket data and meal sales data are correlated as data sources.Based on the neural network method,the supply demand relationship of cold chain catering on high-speed rail is predicted under the condition of known passenger flow.The prediction result can be Provide decision-making guidance and theoretical basis for passenger marketing organizations and operations.The main work of this paper is as follows:Firstly,it analyzes the sales status of high-speed rail meal sales,clarifies the research purpose and significance of sales forecast,and investigates the development history of predictive technology and neural network technology and the research status at home and abroad,and deeply discusses the key technology applications of data warehouse and neural network.The features and problems of this system.Then,combined with the actual situation of high-speed rail meal sales and sales business,the paper further analyzed the functional requirements of the sales forecast management system,and optimized the neural network algorithm,designed the sales forecast management including data acquisition module,predictive analysis module,data visualization module and other functions.The overall structure of the system.Finally,based on the food sales management system as a data source,an experimental example based on historical data for meal sales forecasting is designed and implemented.The ticket data and the meal sales data are first cleaned and converted.As the training data set of the model,they are input to the LSTM neural network and the MLP neural network for training and verification of the prediction results.The experimental results show that the performance of the MLP prediction model is better than the LSTM prediction model.The results of this paper show that the use of neural network theory can provide accurate prediction suggestions for high-speed rail meal sales,and has a strong practical guiding significance for managers' decision-making work.