The invention discloses a
fresh food after-
ripening characteristic and demand prediction transportation aging collaborative optimization method, and relates to the technical field of
fresh food transportation, the method comprises the following steps: using a sensor to collect temperature and
humidity,
gas concentration and
cell metabolite data in a
fresh food after-
ripening process; according to the invention, temperature,
humidity,
gas concentration and
cell metabolite data of the fresh food are collected in real time by using a sensor in a transportation link, and deep fusion and analysis are carried out in combination with multi-source external information such as historical sales data,
social media public opinions, weather disaster early warning and the like, so that precise control of the after-
ripening state of the fresh food is realized; according to the method,
deep learning and association
rule mining technologies are applied, a dynamic
decision model is constructed, and the transportation
time efficiency and the distribution strategy are optimized in real time according to after-ripening characteristics and demand prediction, so that the fresh food loss can be effectively reduced, the product quality is ensured, the transportation
resource utilization rate is improved, the logistics cost is reduced, the market demand is accurately matched, and
stockout and overstock phenomena are reduced.