Machine Learning-Based Logistics Forecasting and Packaging Design Based on Gray Markov Model

Machine Learning (ML) Logistics market analysis provides a comprehensive overview of business models, key strategies, and respective market stocks for some of the most important roles in this landscape. These courses are conducted entirely with in-depth reviews, focusing on impact factors, market point revenue, segment data, regional law data, and national wise data. ML- Logistics demand forecasting plays an important role in optimizing resources and organizational competitiveness. The grey prediction model has the characteristics of low sampling requirements and high prediction accuracy. As expected, it works in a demanding ML-logistics manner. But the prediction method is complicated. Can block its advertisement and applications. We proposed the ML-Logistics Packaging Gray Markov model (LPGMM) for ML-Logistics and packaging forecasting of Material flow forecasting is an important content in the field of programming, management, and ML-Logistics. Integrating the LPGMM for the ML-Logistics industry to take quantitative ML-Logistics in certain ML-Logistics sectors over the next few years, predict the ML-Logistics volume and rate it. The results show that the model is more accurate and reliable than the gray model. Analyze Factors are analyzing the design, density, contrast and color variation Product Packaging goods transfer Cost. In the functions, the ability to quickly integrate the radial base probability of the packaging, modeling and prediction methods can truly learn the code information and predict the flow logistic cost. True prediction, the method is possible and effective.