The invention provides a
fresh food demand prediction method and
system based on a cloud warehouse
system, and the method comprises the steps: firstly obtaining multi-source demand
time sequence data, including historical order fluctuation, environment
perception monitoring and
market activity association sequences, in a preset period of a target region, and then carrying out the
time sequence, space and event
feature coding of each sequence, the method comprises the steps of generating corresponding features, inputting the corresponding features into a regional warehouse group feature update network to obtain regional warehouse group features with dynamic
adaptive capacity, generating predicted demand features containing multi-time
granularity fresh food category demand quantity distribution through a decoder of a demand prediction model, and finally dynamically adjusting inventory configuration parameters of a cloud warehouse
system according to the features. And the deviation rate of the
fresh food reserve of the regional warehouse group and the predicted demand quantity is lower than a preset value, so that the fresh food demand can be comprehensively and accurately predicted, the cloud warehouse inventory configuration is optimized, and the operation efficiency is improved.