【摘要】 为了快速响应终端用户的制造服务需求,提出了智能制造服务系统构建方法,该方法以智能制造服务活动大数据为基础,将制造服务需求转换为智能制造服务方案,并调用智能制造服务模块匹配方案,集成为智能制造服务系统提供给终端用户.在智能工厂、智能生产、智能服务等智能制造服务活动大数据分析基础上,建立了智能制造服务系统模块化的过程模型,该模型以用户域、功能域、制造服务域、流程域、交付域刻画模块化过程的映射关系.结合工业大数据和改进遗传蜂群算法对智能制造服务系统构建过程中智能制造服务模块组合优选问题制定了优选方案,提出了基于反向学习的改进遗传蜂群算法(IGBCOL)的智能制造服务模块组合优选策略.最后通过汽车个性化定制服务模块的算例分析,验证了该算法的可行性,以及工业大数据驱动智能制造服务系统构建方法的有效性.更多还原

【Abstract】 To respond quickly to the manufacturing service needs of end users, a construction method for intelligent manufacturing service systems is proposed. Based on intelligent manufacturing service activity big data, this method transforms manufacturing service requirements into intelligent manufacturing service schemes and then calls the matching scheme of an intelligent manufacturing service module, which is integrated into the intelligent manufacturing service system and provided to the end users. Based on a big data analysis of intelligent manufacturing service activities, such as an intelligent factory, intelligent production, and intelligent service, a modular process model of an intelligent manufacturing service system(IMSS) is established. This model describes the mapping relationship of modular processes in terms of the user, functional, manufacturing service, process, and delivery domains.Combined with the industrial big data and an improved genetic bee colony algorithm, an optimal selection scheme for the service module combination in the IMSS is made, and an improved genetic bee colony algorithm based on reverse learning is proposed to optimize the service module composition in intelligent manufacturing. Finally, through the example analysis of automobile personalized service modules, the feasibility of the algorithm and effectiveness of the construction method of IMSSs driven by big data are verified.更多还原