Abstract: A method of imaging a 3D object, including: obtaining a 3D object generated by a NeRF model in a virtual space; generating a 3D wrapping mesh that completely surrounds the 3D object; sampling color values on the surface of the 3D object based on the 3D wrapping mesh and a 2D mapping mesh mapped to the 3D wrapping mesh, and saves the sampled color values to the 2D mapping mesh; determining, based on a specified viewing angle, a plurality of display rays corresponding to a plurality of mapping pixels of the 2D mapping mesh; obtaining, from the 2D mapping grid, color values of a plurality of display pixels of a 2D display image corresponding to the 3D object in the specified viewing angle based on the display rays; and displaying the display pixels on a display at a user terminal for displaying the 2D display image showing the 3D object.

Abstract: An automated inventory management method is provided. Historical sales states of multiple stores are received, and models of each store and each category of products are pre-trained by a pre-training module. States of each store and each category of products are obtained by a multi-store-multi-category training module, and horizontal and vertical relevance training based on the pre-trained models of each store and each category of products is conducted. Relevance between stores, horizontal relevance between categories of products, and vertical relevance between multiple stores and multiple categories of products are determined by a state analysis module, so that multiple stores and categories of products with high correlation are linked to modify expected sales of each store and each category of products. Orders for multiple categories of products in each store are placed and purchase volumes of multiple categories of products in each store are determined by an inventory decision module.

Abstract: An automated inventory management method is provided. A historical sale record is received, and a future sale of an item is predicted based on the historical sale record to obtain a simulation result of an expected sale state of the item in the next sale cycle. According to the historical sale record of full categories of items and the simulation result of the item in the next sale cycle, an initial weight of the pre-training model is trained and used as a weight of an inventory decision module, and a purchase order that meets the expected sale record of the item in the next sale cycle is automatically generated. A reward feedback is calculated according to a current sale record and an inventory volume of the item and a purchase order of the previous sale cycle and input into the inventory decision module to order the item.

Abstract: The present application discloses a trading decision generation system and method. The trading decision generation method includes: obtaining a market information; performing a market view generation module to generate a market view according to the market information; performing a state integration module to generate a state according to the market information, the market view and a trading information; performing a decision parameter generation module to generate a decision parameter according to the state; and performing a decision generation module to generate a decision according to the decision parameter.

Abstract: An investment strategy rule generation method including the following steps is provided. Firstly, an investment strategy rule generator generates an investment strategy model according to an investment history trend. Then, a total investment return of each of N candidate investment rules is obtained by the investment strategy rule calculator, wherein each of the N candidate investment rules includes a candidate market direction rule. The obtaining step includes: performing an investment simulation in each of multiple time points in a time window of a time interval. Then, the total investment return under the operation of the investment simulations over the time interval is calculated by the investment strategy rule calculator. Then, the candidate investment rule corresponding to the best of the total investment returns is used as an investment strategy rule of the investment strategy model by the investment strategy rule calculator.

Abstract: An investment strategy rule generation method including the following steps is provided. Firstly, an investment strategy rule generator generates an investment strategy model according to an investment history trend. Then, a total investment return of each of N candidate investment rules is obtained by the investment strategy rule calculator, wherein each of the N candidate investment rules includes a candidate market direction rule. The obtaining step includes: performing an investment simulation in each of multiple time points in a time window of a time interval. Then, the total investment return under the operation of the investment simulations over the time interval is calculated by the investment strategy rule calculator. Then, the candidate investment rule corresponding to the best of the total investment returns is used as an investment strategy rule of the investment strategy model by the investment strategy rule calculator.