Collaborative Computing Method for Highly Available Operation of Digital Twin Manufacturing Equipment
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摘要:
在数字孪生技术中，复杂模型和生产逻辑的运行会消耗大量资源，且硬件能力与用户需求存在差异，进而导致模拟精度和实时性难以得到保证，降低系统可用性. 为此，提出一种可视化与逻辑运算协同处理的数字孪生制造装备实时同步计算框架. 首先，根据车间多维度信息构建设备数字模型，考虑硬件能力和用户个性化计算需求，提出可配置、自适应的系统环境映射方法以修正模拟保真度，确保孪生装备的实时准确运行，并以光照环境映射为例说明其流程；然后，提出基于仿真的六自由度机械手运动逻辑解算算法，将渲染帧时作为仿真时钟推进步长，保证模型运动准确以及可视化与解算同步，并将算法泛化，以应用到其他多体设备中；最后，基于Web设计并开发数字孪生车间建模仿真云平台，以六自由度机械手与某转向架构架加工车间为应用对象对所提方法进行验证. 结果表明：随着映射保真度自适应下降，模拟响应速度提升45%，同时GPU和CPU的资源利用率有效降低；证明本文所提方法可实现资源合理配置与系统高效计算，并减少误差累计，是一种高可用的实时协同计算方法.
Abstract:In digital twin technology, the operation of complex models and production logic consumes a large number of resources. Meanwhile, differences in hardware capabilities and user requirements make it difficult to ensure simulation accuracy and real-time performance, reducing system availability. To address this issue, a real-time synchronous computing framework for digital twin manufacturing equipment that collaboratively processed visualization and logic computation was proposed. Firstly, a digital model of the equipment was constructed based on multidimensional workshop information. According to hardware capabilities and users’ personalized computational needs, a configurable and adaptive system environment mapping method was introduced to adjust simulation fidelity, ensuring the real-time and correct operation of the twin equipment. The process was illustrated by using lighting environment mapping as an example. Secondly, a simulation-based motion logic solving algorithm for a six-degree-of-freedom (6-DOF) manipulator was presented, which used the rendering frame time as the simulation clock advancement step to ensure accurate model motion and synchronization between visualization and computation. The algorithm was generalized for application to other multi-body equipment. Finally, a Web-based digital twin workshop modeling and simulation cloud platform was designed and developed. A 6-DOF manipulator and a specific bogie frame processing workshop were used as application cases, and the proposed method was validated. The results show that with the adaptive reduction of mapping fidelity, simulation response speed is increased by 45%, while GPU and CPU resource utilization is effectively reduced. It proves that the method can achieve reasonable resource allocation and efficient system computation while reducing error accumulation, making it a highly available real-time collaborative computing method.
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图 1 数字孪生车间制造装备高可用运行协同计算框架
Figure 1. Collaborative computing framework for highly available operation of manufacturing equipment in digital twin workshop
图 3 可配置、自适应映射服务运行流程
Figure 3. Configurable and adaptive mapping service operating process
图 5 六自由度机械手拓扑节点、控制点示意
Figure 5. Topological nodes and control points of 6-DOF manipulator
图 11 机械手解算与真实设备运动误差对比
Figure 11. Comparison of motion errors between manipulator solution and real equipment
表 1 六自由度机械手拓扑节点定义
Table 1. Topological node definition of 6-DOF manipulator
拓扑节点名称 运动类型 旋转轴（局部坐标） M 静止 S 旋转 y L 旋转 z U 旋转 z R 旋转 x B 旋转 z T 旋转 x P 跟随 W 跟随 
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表 2 映射服务等级与系统运行效率对比表
Table 2. Comparison of mapping service level and system operation efficiency
等级 帧率/
（帧•s−1）步长/
msCPU 利用率/% GPU 利用率/% 方法 1 方法 2 方法 1 方法 2 Ⅰ级 93 10.75 62.8 28.3 2.3 26.9 Ⅱ级 69 14.49 67.0 29.5 2.7 38.7 Ⅲ级 51 19.60 91.3 31.5 3.8 55.2
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