制造物联网协同感知的服务组合优化模型与寻优算法研究

Internet of Manufacturing Things (IOMT) is an important means to realize informationization and intellectualization for the traditional manufacturing process. The manufacturing process is typically dynamic, for example, most manufacturing things are in a fast-flowing state, and the conditions of the manufacturing environment and the state of production processes are also changing dynamically. To ensure the sensing accuracy and completeness, it is necessary to exploit collaborative sensing mechanism for monitoring these dynamic manufacturing processes. .This project proposes to carry out research on the optimization model and algorithm of service composition for collaborative sensing in IOMT, mainly includes: the rapid and effective acquisition method of the actual sensing area and description model for the sensing nodes in manufacturing environment; coverage quality evaluation method of collaborative sensing, a multi-objective mathematical programming model of service combination optimization; constructing the service correlation graph model in functional and spatial domain, the multi-objective game theoretic scheduling method, low-complexity and high-performance search optimization algorithms for service combination in functional and spatial domain. .The primary intellectual merit of the project includes an efficient heuristic event moving method for obtaining the irregular sensing region, a coverage quality evaluation method by considering the deterministic coverage and probability coverage simultaneously, and a multi-objective particle swarm optimization algorithm with a cooperative game theoretic scheduling method in spatial domain. Our study provides a theoretical basis and technical support for collaborative sensing in Internet of Manufacturing Things.

制造物联网是实现传统制造过程信息化、智能化的重要手段，其中人、物料、在制品、产品等制造资源的快速流动、生产过程多种工艺与环境参数动态变化等过程，需要多传感器协同感知来保障其过程监测的数据准确性与完备性。.本项目开展协同感知的服务组合优化模型与寻优算法研究，主要包括：制造环境传感器节点实际感知区域快速高效获取方法与描述模型；协同感知的覆盖质量评价方法，多目标关注的协同感知服务组合优化数学规划模型；传感器节点服务的功能域、空间域关联图模型，多优化目标间的博弈均衡方法以及高效低计算复杂度的功能域、空间域联合搜索寻优算法。.本项目将在启发式事件移动的不规则感知区域快速高效获取方法、确定覆盖与概率覆盖结合的覆盖质量评价方法、基于合作博弈策略的多目标粒子群的空间域服务组合寻优搜索算法等方面形成创新性成果，为制造物联网协同感知提供理论依据与技术支撑。

制造物联网是实现传统制造过程信息化、智能化的重要手段，其中人、物料、在制品、产品等制造资源的快速流动、生产过程多种工艺与环境参数动态变化等过程，需要多传感器协同感知来保障其过程监测的数据准确性与完备性。.本项目开展协同感知的服务组合优化模型与寻优算法研究，主要包括：制造环境传感器节点实际感知区域快速高效获取方法与描述模型；协同感知的覆盖质量评价方法，多目标关注的协同感知服务组合优化数学规划模型；传感器节点服务的功能域、空间域关联图模型，多优化目标间的博弈均衡方法以及高效低计算复杂度的功能域、空间域联合搜索寻优算法。.截止目前，本项目重点研究并构建了智能制造物联网体系结构模型，制造物联工业环境传感器节点非规则感知区域快速检测方法，工业制造环境非规则区域的无线传感器网络节点部署方法，制造物联网协同感知分布式服务组合寻优方法，面向复合事件的异构传感器节点配置优化方法等。在执行期内共取得的研究成果包括：已录用SCI论文5篇，核心期刊论文2篇；投稿EI、SCI论文各1篇；授权发明专利1项，实审发明专利8项。项目按照计划顺利执行，完成项目预定研究目标。.本课题研究制造物联网协同感知的服务组合优化模型与寻优算法，基于制造物联网中感知节点异构与大规模分布、资源受限、环境混杂与多源干扰等特性，研究完成传感器节点感知能力评价与服务形式化描述建模、协同感知的服务组合优化数学规划模型以及快速高效的服务组合搜索寻优算法，为增强制造物联网中动态制造过程协同感知服务组合的灵活性、可靠性，实现其推广应用提供新思路与理论依据。