Data modeling and retrieval re-ranking methods for cloud-based product design processes

An unstructured data modeling and retrieval method tailored for cloud-based product design was proposed to address the challenges of unstructured data processing in product design, and to overcome the limitations of conventional retrieval systems with fixed ranking strategies and lack of precision for specific industry data First, a framework for unstructured data processing was developed to meet the practical needs of innovation and decision-making for cloud-based product design. Next, a novel approach redefined layout analysis of scientific documents as an object detection problem, building a multi-element layout analysis and recognition model within the context of domain-specific scientific document databases. By constructing a data feature space and label features, combined with the LambdaMART algorithm, dynamic ranking and efficient retrieval of domain-specific scientific document data were achieved. Finally, case studies validated the proposed method’s potential for application in product innovation, providing novel support for data-driven design iteration and precise decision-making.