Voxel Data Processing for GPU-Accelerated Game AI
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Solution Overview
Problem
Existing game data processing methods using pixel data are inefficient for high real-time competitive games, particularly in CPU-based systems, as they struggle with calculation efficiency and cannot meet the demands of multiplayer games with AI behaviors.
Innovation Solution
Converting pixel data into voxel data, which is then processed using a GPU-based system, allowing for efficient splicing of voxel modules and inputting voxel data into neural networks for enhanced AI behavior calculation and fraud detection.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If CPU-based behavior tree calculation is used for AI behaviors, then the system can process game logic, but the calculation efficiency is low and cannot meet high real-time requirements
Solution Approach 1:
The patent replaces the CPU-based mechanical calculation system with a GPU-based parallel computing system. By converting the behavior tree data structure into a format suitable for GPU processing and utilizing the GPU's parallel architecture, the system achieves significant improvements in calculation efficiency and real-time response capability for AI behavior computation in multiplayer games.
2Productivity
If pixel data format is used for game scene display, then the scene can be rendered on display screens, but the data processing efficiency is insufficient for high real-time competitive games
Solution Approach 1:
The patent changes the fundamental data representation parameter from pixel format to voxel format. This parameter change enables the data to be processed more efficiently by GPU architectures while maintaining the necessary visual and spatial information for high real-time competitive games, thus improving both processing efficiency and calculation reliability.
3Productivity
If traditional behavior tree traversal is performed on CPU, then AI behaviors can be calculated, but the method consumes excessive CPU performance and cannot meet business needs
Solution Approach 1:
The patent substitutes the CPU's sequential processing mechanism with the GPU's parallel processing mechanism for AI behavior calculation. By transferring the behavior tree traversal task from CPU to GPU, the system dramatically reduces CPU performance consumption while accelerating AI behavior calculation speed through parallel execution of multiple node evaluations simultaneously.
Data Source
AI summary
The present invention provides a data processing method and server based on voxel data, a medium, and a computer program product, and the method includes: exporting original data of multiple types of scene elements respectively; setting an expected side length of a unit voxel, combined with the side length of the unit voxel, converting the original data of the multiple types of scene elements into voxel data respectively, where the voxel data is represented as a voxel module in the voxel scene; and according to relative positions of the voxel modules of all the scene elements in the pixel scene, splicing the voxel modules of all the scene elements to obtain the voxel scene. In the present invention, for calculation of spatial data, pixel data is converted into the voxel data, and this is highly compatible with GPU computing. Compared with the existing CPU computing mode, the performance of the GPU-based data computing solution is 2 to 3 orders of magnitude higher than that of the CPU-based computing mode.