Dynamic personalized orders demand and uncertain manufacturing resource availability have become the research hotspots of intelligent resource optimization allocation. Currently, the data generated from the manufacturing industry are rapidly expanding. Such data are multi-source, heterogeneous and multi-scale. Transforming the data into knowledge to optimize the allocation between personalized orders and manufacturing resources is an effective strategy to improve the cognitive intelligent production level of enterprises. However, the manufacturing processes in resource allocation is diversity. There are many rules and constraints among the data. And the relationship among data is more complicated. There lacks a unified approach to information modeling and industrial knowledge generation from mining semantic information from massive manufacturing data. The research challenge is how to fully integrate the complex data of workshop resources and mine the implicit semantic information to form a viable knowledge-driven resource allocation optimization method. Such method can then efficiently provide the relevant engineering information needed for resource allocation. This research presented a unified knowledge graph-driven production resource allocation approach, allowing fast resource allocation decision-making for given order inserting tasks, subject to the resource machining information and the device evaluation strategy. The workshop resource knowledge graph (WRKG) model was presented to integrate the engineering semantic information in the machining workshop. A distributed knowledge representation learning algorithm was developed to mine the implicit resource information for updating the WRKG in real-time. Moreover, a three-staged resource allocation optimization method supported by the WRKG was proposed to output the device sets needed for a specific task. A case study of the manufacturing resource allocation process task in an aerospace enterprise was used to demonstrate the feasibility of the proposed approach.

动态个性化订单需求和不确定的制造资源可用性已成为智能资源优化分配的研究热点。当前，从制造业产生的数据正在迅速扩展。这样的数据是多源，异构和多尺度的。将数据转化为知识以优化个性化订单和制造资源之间的分配是提高企业认知智能生产水平的有效策略。但是，资源分配中的制造过程是多样性的。数据之间有许多规则和约束。并且数据之间的关系更加复杂。缺乏从大量制造数据中挖掘语义信息来进行信息建模和工业知识生成的统一方法。研究的挑战是如何充分整合车间资源的复杂数据，挖掘隐含的语义信息，以形成可行的知识驱动型资源分配优化方法。然后，这种方法可以有效地提供资源分配所需的相关工程信息。这项研究提出了一种统一的知识图驱动的生产资源分配方法，该方法可以根据资源加工信息和设备评估策略，针对给定的订单插入任务快速进行资源分配决策。提出了车间资源知识图（WRKG）模型，以集成加工车间的工程语义信息。开发了一种分布式知识表示学习算法，以挖掘隐式资源信息以实时更新WRKG。此外，提出了WRKG支持的三阶段资源分配优化方法来输出特定任务所需的设备集。通过对某航天企业制造资源分配过程任务的案例研究，证明了该方法的可行性。提出了WRKG支持的三阶段资源分配优化方法，以输出特定任务所需的设备集。通过对某航天企业制造资源分配过程任务的案例研究，证明了该方法的可行性。提出了WRKG支持的三阶段资源分配优化方法，以输出特定任务所需的设备集。通过对某航天企业制造资源分配过程任务的案例研究，证明了该方法的可行性。