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As an important source of inspiration, the great number of patent documents provides designers with valuable knowledge of design rationale (DR), including issues, intent, pros and cons of the solutions. Researchers have carried out a number of data analysis studies based on patent information, which is now a new discipline called Patinformatics, including the analysis of patent information from a macro perspective and the identification and extraction of patent knowledge from a micro perspective. If DR knowledge could be extracted automatically from the patent documents and provided to designers as a source of inspiration, it would greatly promote innovative design, and at the same time promote the reuse of patent documents and the wide application of DR theory, which can be like killing three birds with one stone. To address this issue, this study proposes an improved lexical-syntactic pattern method for DR centric patent knowledge extraction, including DR Vector Space model (DRVS), DRV Trigger Word (DRV-TW), Design Rationale Vector (DRV), DR credibility (DRC) and others, and DRV based knowledge extraction algorithms. Knowledge extraction experiments were conducted on 1491 patent documents to verify the feasibility and performance of the method. In addition, two other sets of comparative experiments were conducted using the FastText and BERT machine learning methods, and the results further confirmed the reliability of the proposed method for lowresource corpus.INDEX TERMS Patent analysis, design rationale, knowledge extraction, design knowledge network.

The research of demand forecast of city emergency supplies is of great significance to the management of urban emergency logistics. Taking the city public health emergency as the background, the paper will establish a model of push -pull demand forecasting about solving the problem of emergency supplies which is efficient and economical in emergency supplies efficiency under an emergency situation. The grey prediction method is used to solve the model by using the example analysis. The result shows the feasibility of the model and algorithm.

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A large number of publicly available documents, including patent documents and journal articles, can provide designers with creative stimuli, which could facilitate product innovation and collaborative design. As an important tacit knowledge, the acquisition, sharing, and reuse of design rationale (DR) is of great value to designers, which could help designers to better understand design intentions and ideas, support design automation, and promote better collaborative design. However, due to the fragmentation of DR in documentation, this hinders designer acquisition and reuse. If the DR fragments could be automatically extracted from the technical documents to build an interconnected knowledge network system, the problem would be effectively solved, which would further promote the development and utilization of digital archives. To address this issue, this study proposes a three-dimensional design knowledge network metamodel, Design Knowledge Semantic Network (DKSN), and a DKSN-based knowledge fusion method for the construction of a Design Knowledge Network (DKN). We set up an empirical experiment to verify the feasibility and performance of the method. Patent documents and open access research articles are used as sample documents, and a product data dictionary imported from ISO/TS 23768-1 is used as the predefined artifact dictionary. The results further confirm the feasibility and good application prospects of the proposed method.