基于推理-情境感知激活模型的设计知识推荐

针对个性化产品设计任务下的知识资源检索和利用效率低、知识推荐结果有效性和准确性侧重失衡的问题,提出个性化设计知识推荐服务架构.该架构以产品设计文献数据、手册数据为核心,结合产品设计场景特征,确定设计知识类别.采用新的深度学习模型ALBERT-BiLSTM-IDCNN-CRF,对数据中蕴含的设计对象、设计任务、设计性能、设计方法、设计工具、设计原理、设计参数、计算公式、设计图这9类知识元进行有效抽取.结合由企业内部员工和文献作者组成的设计人员知识元,建立五维设计知识关联模型,以该模型为基础,运用本体和推理规则建立完善的产品设计知识库.提出基于推理-情境感知激活模型的设计知识推荐服务模式.以液压机设计为例进行验证.结果表明,所提的深度学习模型在设计知识抽取中获得了优于其他基准模型的效果.所提的知识推荐服务模式在维持较高的知识推荐结果准确性的同时,提升了输出推荐结果的有效性,可以快速、准确地为特定对象属性、任务属性和性能要求属性下的个性化设计任务求解推荐所需的知识资源.

Design knowledge recommendation based on inference-context-aware activation model

A personalized design knowledge recommendation service architecture was proposed aiming at the problems of low efficiency of knowledge resource retrieval and utilization and imbalance of effectiveness and accuracy of knowledge recommendation results under the task of personalized product design. The framework combined the product design scene characteristics to determine the design knowledge categories based on the product design literature data and manual data. A new deep learning model ALBERT-BiLSTM-IDCNN-CRF was adopted to effectively extract 9 types of knowledge elements contained in the data, including design objects, design tasks, design performance, design methods, design tools, design principles, design parameters, calculation formulas and design drawings. A five-dimensional design knowledge association model was established by combining the knowledge elements of designers composed of internal employees and literature authors. A complete product design knowledge base was established by using ontology and inference rules based on the model. A design knowledge recommendation service mode based on inference-context-aware activation model was proposed. The hydraulic press design was taken as an example to verify. Results show that the proposed deep learning model achieves better results than other benchmark models in design knowledge extraction. The proposed knowledge recommendation service mode greatly improves the effectiveness of outputting recommendation results while maintaining a high accuracy of the knowledge recommendation results. Required solution knowledge resources can be quickly and accurately recommended for personalized design tasks under specific object attributes, task attributes and performance requirement attributes.