无规则数字孪生：通过标准化框架与技术实现声明式决策
Ruleless Digital Twins: Toward Declarative Decision-Making Through Standardized Frameworks and Technologies
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中文总结 AI 辅助
该研究提出无规则数字孪生（RDTs），结合语义知识库等标准化组件实现声明式决策，经虚拟办公室实验，其控温与节能效果优于规则式双位控制器，但长预测时域需较强算力。
中文摘要 AI 辅助
数字孪生（DTs）可视为物理对象的数字对应物，或更广义的孪生目标（TTs）。为对孪生目标的属性进行变更并优化，数字孪生需运用决策机制。传统上，众多领域（如家庭自动化）采用基于规则的决策模型，依据期望的孪生目标条件以命令式方式定义数字孪生的动作。随着用户需求的演变，基于规则的模型的开发与维护愈发复杂，尤其适用于动态变化系统下的优化场景，例如受天气或动态能源定价影响的场景。我们提出一种替代方案：无规则数字孪生（RDTs），其能基于纯声明式用户规范自动生成最优决策，与成熟的无规则模型预测控制方法类似。该方案通过语义知识库、逻辑推理、仿真模型及自主计算架构的组合实现，各组件均基于广泛使用的标准或技术。我们通过孵化器案例研究及虚拟办公室环境下与基于规则的双位控制器的对比实验，对概念验证进行评估。结果显示，在动态变化的实时定价下，无规则数字孪生在维持期望室温及最小化能源成本方面的核心功能优于对照组。此外，由于组合决策树的构建，无规则数字孪生在较长预测时域下需要相当大的计算能力，不过其对特定问题领域的适用性（及较短时域的使用）最终取决于用户。
英文摘要
Digital twins (DTs) can be thought of as digital counterparts of physical objects, or more generally, twinning targets (TTs). To enact changes on and optimize for properties of their TTs, DTs use an element of decision-making. Traditionally, many domains, such as home automation, utilize rule-based decision-making models to imperatively define DT actions based on desired TT conditions. With evolving user specifications, rule-based models have become increasingly complex to develop and maintain, particularly in scenarios involving optimizations under dynamically changing systems, such as those affected by weather or dynamic energy pricing. We present an alternative in the form of ruleless digital twins (RDTs) that automatically produce optimal decisions with respect to purely declarative user specifications, similarly to the well-established ruleless approach of model predictive control. They do so through a combination of a semantic knowledge base, logical inference, simulation models, and an autonomic computing architecture, each exclusively based on a widely-used standard or technology. We evaluate our proof of concept through an incubator case study and experiments against a rule-based bang-bang controller in the context of a virtual office room environment. Results show core that RDT functionality performs better than the control in terms of maintaining desired room temperatures and minimizing energy costs under dynamically changing spot pricing. Additionally, due to combinatorial decision tree construction, RDTs require considerable computational power for longer prediction horizons, although their applicability to specific problem domains (and use of shorter horizons) ultimately falls on their users.
发表机构
- Western Norway University of Applied Sciences（西挪威应用科学大学）
机构由 AI 辅助整理，请以论文原文为准。
