Nomenclature.

Human-robot interaction has gained significant attention in various domains, including healthcare, customer service, and industrial automation. High computational cost, inefficient service matching, and elevated failure rates in dynamic service contexts are some primary disadvantages of existing query-processing systems. This research introduces a Hybrid Intelligent Computing Model (HICM) to improve robots’ ability to process inquiries autonomously. The goal is to make robots better at responding to human questions in real time with efficient, personalized, and context-specific solutions. Using self-organized computing approaches, robotic agents can reliably provide end-users with services suited to their demands. Due to their autonomous nature, robots must be able to calculate quickly and accurately to provide timely services. To meet these needs, the proposed HICM incorporates a sophisticated decision-support system to handle human questions and find the appropriate services. Within this decision-making framework, the model evaluates the characteristics and relevance of questions about accessible services by combining annealing and Tabu Search approaches. To avoid addressing queries incompatibly, the Tabu Search technique approaches query resolution as a non-convergent optimization issue. Comparing HICM’s performance to other models reveals significant improvements over CDS, DGTA, and CCS. In particular, HICM reduced calculation time by 8.67%, service time by 15.09%, and failure rates by 7.87%. In terms of important metrics, HICM fared better than the competing models. Its success factor was 11.8% higher, its matching ratio was 14.88% higher, and its failure rates were 6.22% lower. These findings demonstrate the model’s efficiency and reliability in terms of robotic query processing and real-time service delivery.

人机交互（Human-Robot Interaction）在医疗健康、客户服务与工业自动化等诸多领域已受到广泛关注。现有查询处理系统存在多项核心弊端：计算成本高昂、服务匹配效率低下，且在动态服务场景下故障发生率偏高。本研究提出一种混合智能计算模型（Hybrid Intelligent Computing Model, HICM），以提升机器人自主处理查询请求的能力，旨在使机器人能够借助高效、个性化且贴合场景的解决方案，更出色地实时回应人类问询。
通过自组织计算方法，机器人智能体（AI Agent）可为终端用户可靠提供契合其需求的服务。鉴于机器人具备自主属性，其必须能够快速且精准地完成计算，以保障服务的及时交付。为满足上述需求，所提出的混合智能计算模型集成了一套先进的决策支持系统，用于处理人类问询并匹配适配的服务。在该决策框架内，本模型结合模拟退火（Annealing）与禁忌搜索（Tabu Search）方法，对与可用服务相关的问询的特征与相关性进行评估。为避免对问询进行不兼容处理，禁忌搜索方法将查询求解视作非收敛优化问题。
将混合智能计算模型的性能与其他模型对比后可见，其在CDS、DGTA与CCS模型上均实现了显著性能提升。具体而言，该模型将计算时长缩短8.67%、服务耗时降低15.09%、故障发生率下降7.87%。在关键指标方面，混合智能计算模型的表现均优于对比模型：其成功因子提升11.8%，匹配率提高14.88%，故障发生率降低6.22%。
上述研究结果证实，该模型在机器人查询处理与实时服务交付方面具备高效性与可靠性。