Human-robot interaction (HRI) is a multidisciplinary area that consists of several technologies that are used to create various smart city applications. The knowledge gain and analysis of the smart city environment improves response time. This paper introduces the dependable information processing (DIP) method for handling multi-attribute environmental information in a smart city application. Information sensed from the environment is categorized in the initial stage regarding how it meets application requirements. It helps to identify the need and response of the application through different interacting spans and previous trials. For attribute categorization and span validation, learning via K-means clustering is exploited to identify similar and dissimilar information attributes. This identification speeds up the process of successive responses with improved interaction sessions. The accuracy of user responses and latency-free assessment improves the reliability of DIP in smart city applications. The efficiency of the system is then evaluated using detection accuracy (97.34%), the response time (4.3 s), and interaction time (12.3 s), which is compared with other methods.

人机交互（HRI）是一个多学科领域，包含用于创建各种智能城市应用程序的多种技术。智慧城市环境的知识获取和分析可以缩短响应时间。本文介绍了在智能城市应用中处理多属性环境信息的可靠信息处理（DIP）方法。从环境中感测到的信息在初始阶段就被分类为如何满足应用程序要求的信息。通过不同的交互范围和先前的试验，它有助于确定应用程序的需求和响应。对于属性分类和跨度验证，利用通过K均值聚类的学习来识别相似和不相似的信息属性。这种识别通过改进的交互会话加快了连续响应的过程。用户响应的准确性和无延迟评估提高了智能城市应用中DIP的可靠性。然后使用检测精度（97.34％），响应时间（4.3 s）和交互时间（12.3 s）评估系统的效率，并将其与其他方法进行比较。