Model training method for determining order quantity, method and device for determining order quantity

Abstract

The present disclosure provides a model training method, a demand determination method, and an apparatus, wherein the model training method includes: for each first historical time period in a plurality of first historical time periods, obtaining data obtained in the first historical time period The number of orders for each grid area pair in the internal model; input the order quantity of each grid area pair in the first historical period of the previous preset number into the original model to obtain the first reference order quantity; The order quantity of each grid area pair in a historical time period is input into the compression model, and the second reference order quantity is obtained; based on the first reference order quantity, the second reference order quantity and each grid area in the last first historical time period The compressed model is trained on the number of true orders. The embodiment of the present disclosure compresses the original model through knowledge distillation, and the obtained compressed model uses fewer parameters, which can reduce the consumption of computing resources in the process of determining the number of orders.

Description

technical field [0001] The present disclosure relates to the field of computer technology, and in particular, to a model training method, a demand determination method, and an apparatus. Background technique [0002] With the development of Internet technology, online booking travel service has gradually become an important choice for people to travel. Passengers can place travel orders online from the origin to the destination according to the travel plan. In order to better meet the travel needs of passengers, the number of orders between each origin and destination in the future can be determined based on the number of previous orders, and then the determined number of orders can be used to help dispatchers make more reasonable scheduling for each location. vehicle. [0003] But usually, the process of determining the order quantity needs to consume a lot of computing resources, which brings a large workload to the computing device. SUMMARY OF THE INVENTION [0004] ...

AI Technical Summary

Problems solved by technology

[0003] However, under normal circumstances, the process of determining the order quantity needs to consume a large amount of computing resources, which brings a large workload to the computing equipment.

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