基于物理仿真与本地离线AI复盘分析的台球数字化训练系统研究

Research on Billiards Digital Training System Based on Physical Simulation and Local Offline AI Replay Analysis

摘要: 针对传统台球训练缺少量化运动数据、智能复盘依赖云端算力、多球碰撞统计失真等现实痛点，本文依托PyMunk二维物理引擎与PyGame可视化框架，构建一体化台球仿真与离线AI复盘训练系统。系统引入碰撞冷却校验机制，消除连续撞击重复计数误差，下文补充该机制完整算法流程图、伪代码，并详细阐释冷却时长核心参数的选取依据，设计双维度运动可视化图表完整记录白球位移轨迹、全周期速度衰减特征；集成透视视角变换、瞄准锁定、力度调节、白球点位调整多通道人机交互单元；搭建轻量化本地规则AI复盘模块，自动提取撞击次数、进球数、击球力度、平均球速等多维指标，分层输出击球精度评价与训练优化方案。仅引入球体碰撞距离判定单公式，无复杂数学推导。多组仿真对照实验结果表明，系统球体碰撞统计误差低于1.2%，白球运动轨迹还原完整度98.7%，AI复盘单次分析耗时不足6 ms，可完全离线本地运行，无需网络与高性能显卡。该系统开源轻量化、部署零成本，适配校园体育教学与业余自主训练场景，为室内小球类数字仿真、本地智能复盘融合提供可落地的技术方案。

Abstract: In response to the practical pain points of traditional billiards training, such as the lack of quantitative motion data, reliance on cloud computing power for intelligent replay, and distortion of multi-ball collision statistics, this article relies on the PyMunk 2D physics engine and PyGame visualization framework to construct an integrated billiards simulation and offline AI replay training system. The system introduces a collision cooling verification mechanism to eliminate repeated counting errors caused by continuous impacts. The algorithm flow chart and pseudo-code of the mechanism are supplemented in the subsequent chapters, and the selection basis of core parameter cooling duration is explained in detail. And it designs a two-dimensional motion visualization chart to fully record the displacement trajectory of the white sphere and the full cycle velocity attenuation characteristics; integrates perspective angle transformation, aiming and locking, force adjustment, and white ball point adjustment multi-channel human-machine interaction unit; builds a lightweight local rule AI review module that automatically extracts multidimensional indicators such as impact frequency, goal count, hitting power, average ball speed, etc., and outputs a layered evaluation of hitting accuracy and training optimization plan. Only a single formula for determining the collision distance of spheres is introduced, without complex mathematical derivation. The results of multiple simulation comparison experiments show that the statistical error of the system’s sphere collision is less than 1.2%, the completeness of the white sphere motion trajectory restoration is 98.7%, and the AI replay single analysis time is less than 6 ms, and it can run completely offline locally without the need for network and high-performance graphics card. The system is open-source, lightweight, and has zero deployment cost. It is suitable for campus physical education teaching and amateur autonomous training scenarios, providing practical technical solutions for indoor small ball digital simulation and local intelligent replay integration.