为满足离散制造系统的数字孪生模型快速构建与灵活配置需求，针对现有模型表述不统一、动态适应性差及复用效率低等问题，提出一种离散制造系统数字孪生机理模型库构建方法。首先，定义涵盖几何、物理、行为、规则和接口多维信息的表达范式，利用Modelica元模型实现异构信息的结构化表述，并设计全要素多层级模型的组织集成机制。其次，提出基于FMI标准的规范化封装方法，定义可配置参数与可更新变量，实现配置文件驱动的模型实例化与实时数据驱动的模型参数更新方法。在此基础上，设计并开发层次化模型库平台，实现对封装后模型文件及其数据的统一存储、管理与应用。最后，通过SVPWM组件、主轴子系统及典型离散制造产线开展多层级验证。结果表明，所提平台与关键技术能够提升机理模型的组织管理与复用能力，为离散制造系统数字孪生的快速构建提供支撑。

To meet the requirements for rapid construction and flexible configuration of digital twin models for discrete manufacturing systems, a construction method for a digital twin mechanism model library is proposed to address the problems of inconsistent representation, poor dynamic adaptability, and low reuse efficiency of existing models. First, a representation paradigm covering multi-dimensional information regarding geometry, physics, behavior, rules, and interfaces is defined. The Modelica meta-model is utilized to achieve the structured representation of heterogeneous information, and an organizational integration mechanism for all-element and multi-level models is designed. Second, a standardized encapsulation method based on the Functional Mock-up Interface (FMI) standard is proposed to define configurable parameters and updatable variables, realizing model instantiation driven by configuration files and parameter updates driven by real-time data. On this basis, a hierarchical model library platform is designed and developed, providing unified support for the storage, management, and application of encapsulated model files and related data. Finally, multi-level verification is conducted using the Space Vector Pulse Width Modulation (SVPWM) component, the spindle subsystem, and a typical discrete manufacturing production line. The results show that the proposed platform and key technologies can improve the organization, management, and reuse of mechanism models, thereby supporting the rapid construction of digital twins for discrete manufacturing systems.