水冷式集中空调系统是一个多变量耦合的非线性系统，运行数据稀疏程度大，导致基于数据驱动的机器学习模型泛化能力差，全面反映水力与传热机理的物理模型成为当前研究的关键。然而，系统变量耦合复杂，迭代嵌套导致整体计算量消耗巨大，设备启停又导致管路水力结构变化而不得不频繁重构模型等技术难点亟待解决。利用离散变量连续化及模式搜索法，以阻力系数关联支路开度与设备启停，以水泵运行频率关联水泵启停与运行状态，实现将离散变量整合至连续变量，减少迭代嵌套，可实现流量动态分配、水力热力全局耦合计算。该研究构建了冷负荷、冷冻水流量、冷冻水供水温度、冷冻水供回水压差及环境温湿度为外部约束的水冷式集中空调冷源系统多变量耦合物理模型，实现对冷水机组台数、冷冻水泵台数与频率、冷却水泵台数与频率、冷却塔台数与频率等多个独立变量的异步调节。通过综合实验平台验证模型的可靠性，探究了不同工况下的机塔泵运行特性与群控策略。研究表明，冷源系统物理模型仿真结果平均相对误差小于10%，少部分在15%内，单次迭代计算耗时约0.32 s，多变量组合调节可综合权衡各子系统的能效，系统全局优化可最大限度挖掘节能空间，解决传统主观经验控制难以维持稳定节能效果的缺陷，为智能诊断提供理论基础。

The water-cooled centralized air-conditioning system is a multi-variable coupled nonlinear system with a high degree of sparsity in operational data, leading to poor generalization ability of data-driven machine learning models. A comprehensive physical model reflecting the hydraulic and heat transfer mechanisms has become a key focus of current research. However, there are some technical challenges that urgently need to be addressed, such as the complexity of variable coupling in the system, nested iterations leading to significant computational costs, and that changes in hydraulic structure due to equipment start-stop cycles necessitate frequent model reconstructions. By using continualization of discrete variables and pattern search methods to correlate resistance coefficients with branch openings, and equipment start-stop events with pump operating frequencies, it is possible to integrate discrete variables into continuous ones, reduce nested iterations, and achieve dynamic flow distribution and global hydraulic-thermal coupling calculations. This study established a multi-variable coupled physical model of a water-cooled centralized air-conditioning cold source system with external constraints such as cooling load, chilled water flow rate, chilled water supply temperature, chilled water supply-return pressure difference, and ambient temperature and humidity, enabling asynchronous adjustments of multiple independent variables including the number of chiller units, the number of chilled water pump units and frequencies, the number of cooling water pump units and frequencies, and the number of cooling tower units and frequencies. The reliability of the model was validated through a comprehensive experimental platform to explore the operational characteristics and group control strategies of chiller units, cooling towers, chilled water pumps, and cooling water pumps under different operating conditions. The research findings indicate that the simulation results of the cold source system physical model have an average relative error of less than 10%, with a few cases within 15%. The computational time for a single iteration is approximately 0.32 s. The adjustment of multiple variables can comprehensively balance the energy efficiency of each subsystem. Global optimization of the system can maximize energy-saving opportunities, addressing the shortcomings of traditional subjective empirical control in maintaining stable energy-saving effects and providing a theoretical basis for intelligent diagnostics.