Title: CPN-Py: A Python-Based Tool for Modeling and Analyzing Colored Petri Nets Show Chinese title

Title: CPN-Py：基于Python的有色Petri网建模与分析工具

Abstract: Colored Petri Nets (CPNs) are an established formalism for modeling processes where tokens carry data. Although tools like CPN Tools and CPN IDE excel at CPN-based simulation, they are often separate from modern data science ecosystems. Meanwhile, Python has become the de facto language for process mining, machine learning, and data analytics. In this paper, we introduce CPN-Py, a Python library that faithfully preserves the core concepts of Colored Petri Nets -- including color sets, timed tokens, guard logic, and hierarchical structures -- while providing seamless integration with the Python environment. We discuss its design, highlight its synergy with PM4Py (including stochastic replay, process discovery, and decision mining functionalities), and illustrate how the tool supports state space analysis and hierarchical CPNs. We also outline how CPN-Py accommodates large language models, which can generate or refine CPN models through a dedicated JSON-based format. Show Chinese abstract

Abstract: 带数据的有色 Petri 网（CPNs）是一种用于建模携带数据的令牌的过程的成熟形式化方法。尽管像 CPN Tools 和 CPN IDE 这样的工具在基于 CPN 的仿真方面表现出色，但它们通常与现代数据科学生态系统分离。同时，Python 已成为过程挖掘、机器学习和数据分析的事实标准语言。在本文中，我们介绍了 CPN-Py，这是一个忠实保留有色 Petri 网核心概念的 Python 库——包括颜色集、定时令牌、守卫逻辑和分层结构——同时与 Python 环境提供无缝集成。我们讨论了其设计，强调了它与 PM4Py 的协同作用（包括随机重放、过程发现和决策挖掘功能），并展示了该工具如何支持状态空间分析和分层 CPNs。我们还概述了 CPN-Py 如何适应大型语言模型，这些模型可以通过专用的基于 JSON 的格式生成或优化 CPN 模型。