摘要:信息物理系统（cyber-physical system，简称CPS）是一个在环境感知的基础上整合了物理和计算元素的系统，它可以智能地响应真实世界的动态变化，具有重要而广阔的应用前景.然而，CPS工作在复杂的物理环境中，周围的物理变化会对CPS的行为产生影响.因此，确保CPS在复杂环境中的安全性和可靠性至关重要.提出了一种面向实时数据的一体化建模方法，通过定义一系列的规则，将领域环境模型组合到运行时验证过程中去，从而保证CPS在不确定环境中的安全性和可靠性.该方法首先为环境建立数学模型.然后，设计合并规则将相同系统参数下仅有一个环境影响因子的数学模型合并为相同系统参数下有一个或多个环境影响因子的数学模型.之后，定义转换规则，将数学模型转换为伪代码表示的环境模型.最后，根据组合规则将环境模型组合到运行时监视模型中执行验证.该方法使得监视模型更加完整、准确，当环境发生变化时，通过动态调整参数范围使得CPS中的安全属性在复杂的物理环境中仍然得以满足.将该方法应用到移动机器人避障实验中，对影响电池容量的温度和湿度进行数学建模，然后将环境模型组合到监视模型中去，最终实现在执行任务前可以根据不同的物理环境准确地给出续航时间安全提醒.



Abstract:Cyber-physical systems (CPS) is a system that integrates physical and computational elements based on context-awareness. It can intelligently respond to dynamic changes in the real world and has important and broad application prospects. However, CPS works in a complex physical environment, and changes around it can affect the behavior of CPS. Therefore, ensuring the safety and reliability of CPS in complex environments is critical. This study proposes an integrated modeling method for real-time data. By defining a series of rules, the domain environment model is combined into the runtime verification process to ensure the safety and reliability of CPS in an uncertain environment. First, the method builds a mathematical model for the environment. Then, design the merge rules to merge the mathematical models with only one environmental factor under the same system parameter into a mathematical model with one or more environmental factors under the same system parameter. Next, the transformation rules are defined to convert the mathematical model into an environment model represented by pseudocode. Finally, the environment model is combined into the runtime monitoring model to perform verification according to the combination rules. The method makes the verification process more complete and accurate. When the environment changes, it ensures that the safety properties in the CPS are still satisfied by dynamically adjusting the parameter range. Finally, the method is applied to the mobile robot obstacle avoidance experiment, to model the temperature and humidity physical environment and then, to combine it into the monitoring model, eventually, the life time safety reminder is accurately given in different environments.



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