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Research on intelligent design of debugging processes for complex electronic products based on knowledge graphs.

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Title: Research on intelligent design of debugging processes for complex electronic products based on knowledge graphs.
Authors: Xue, Hao1,2 (AUTHOR) scuxuehao@163.com, Zhao, Wu1,2 (AUTHOR), Chen, Xinyu1,2 (AUTHOR), Guo, Xin1,2 (AUTHOR), Yu, Miao1,2 (AUTHOR), Liu, Yuhan1,2 (AUTHOR), Yan, Dejin3 (AUTHOR), Xiao, Yong3 (AUTHOR), Zhang, Kai1,2 (AUTHOR) zhangkai@scu.edu.cn
Source: Journal of Intelligent Manufacturing. Jan2026, Vol. 37 Issue 1, p505-524. 20p.
Subjects: Debugging, Knowledge graphs, Process optimization, Electronic equipment, Design techniques, Information retrieval, Bayesian analysis
Abstract: The debugging process scheme is pivotal in ensuring that electronic products meet the expected quality and performance standards prior to market launch. Its design process involves the meticulous management of diverse resources and process information. Traditional approaches typically rely on the manual extraction of design knowledge from large volumes of debugging data, a method that is not only inefficient but also hinders the effective reuse and management of knowledge. To address this, this paper proposes an intelligent design methodology for the debugging process of complex electronic products, grounded in knowledge graph techniques, aimed at expanding the design knowledge space and enhancing design efficiency. Initially, based on the structural composition of the debugging process scheme, this paper introduces a five-dimensional feature knowledge ontology to construct the debugging process design knowledge model, and leverages knowledge graphs to enable the structured representation and management of knowledge. Next, based on the debugging process units and process pathways, a knowledge retrieval algorithm is proposed to support the design of the debugging process. Subsequently, the debugging process knowledge is configured by training a Bayesian network model, which generates a structured tree of debugging solutions. These solutions are then optimized and customized to meet the specific requirements of the product. Finally, based on the aforementioned methodology, this paper develops a system tool to support the intelligent design of debugging processes for complex electronic products, which is validated through a case study on the design of a debugging process scheme for communication navigation and identification products. Experimental results demonstrate that the system effectively assists designers in reusing debugging design knowledge and shortening the design cycle. [ABSTRACT FROM AUTHOR]
Copyright of Journal of Intelligent Manufacturing is the property of Springer Nature and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
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  Data: Research on intelligent design of debugging processes for complex electronic products based on knowledge graphs.
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  Data: <searchLink fieldCode="AR" term="%22Xue%2C+Hao%22">Xue, Hao</searchLink><relatesTo>1,2</relatesTo> (AUTHOR)<i> scuxuehao@163.com</i><br /><searchLink fieldCode="AR" term="%22Zhao%2C+Wu%22">Zhao, Wu</searchLink><relatesTo>1,2</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Chen%2C+Xinyu%22">Chen, Xinyu</searchLink><relatesTo>1,2</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Guo%2C+Xin%22">Guo, Xin</searchLink><relatesTo>1,2</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Yu%2C+Miao%22">Yu, Miao</searchLink><relatesTo>1,2</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Liu%2C+Yuhan%22">Liu, Yuhan</searchLink><relatesTo>1,2</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Yan%2C+Dejin%22">Yan, Dejin</searchLink><relatesTo>3</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Xiao%2C+Yong%22">Xiao, Yong</searchLink><relatesTo>3</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Zhang%2C+Kai%22">Zhang, Kai</searchLink><relatesTo>1,2</relatesTo> (AUTHOR)<i> zhangkai@scu.edu.cn</i>
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  Data: <searchLink fieldCode="JN" term="%22Journal+of+Intelligent+Manufacturing%22">Journal of Intelligent Manufacturing</searchLink>. Jan2026, Vol. 37 Issue 1, p505-524. 20p.
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  Data: <searchLink fieldCode="DE" term="%22Debugging%22">Debugging</searchLink><br /><searchLink fieldCode="DE" term="%22Knowledge+graphs%22">Knowledge graphs</searchLink><br /><searchLink fieldCode="DE" term="%22Process+optimization%22">Process optimization</searchLink><br /><searchLink fieldCode="DE" term="%22Electronic+equipment%22">Electronic equipment</searchLink><br /><searchLink fieldCode="DE" term="%22Design+techniques%22">Design techniques</searchLink><br /><searchLink fieldCode="DE" term="%22Information+retrieval%22">Information retrieval</searchLink><br /><searchLink fieldCode="DE" term="%22Bayesian+analysis%22">Bayesian analysis</searchLink>
– Name: Abstract
  Label: Abstract
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  Data: The debugging process scheme is pivotal in ensuring that electronic products meet the expected quality and performance standards prior to market launch. Its design process involves the meticulous management of diverse resources and process information. Traditional approaches typically rely on the manual extraction of design knowledge from large volumes of debugging data, a method that is not only inefficient but also hinders the effective reuse and management of knowledge. To address this, this paper proposes an intelligent design methodology for the debugging process of complex electronic products, grounded in knowledge graph techniques, aimed at expanding the design knowledge space and enhancing design efficiency. Initially, based on the structural composition of the debugging process scheme, this paper introduces a five-dimensional feature knowledge ontology to construct the debugging process design knowledge model, and leverages knowledge graphs to enable the structured representation and management of knowledge. Next, based on the debugging process units and process pathways, a knowledge retrieval algorithm is proposed to support the design of the debugging process. Subsequently, the debugging process knowledge is configured by training a Bayesian network model, which generates a structured tree of debugging solutions. These solutions are then optimized and customized to meet the specific requirements of the product. Finally, based on the aforementioned methodology, this paper develops a system tool to support the intelligent design of debugging processes for complex electronic products, which is validated through a case study on the design of a debugging process scheme for communication navigation and identification products. Experimental results demonstrate that the system effectively assists designers in reusing debugging design knowledge and shortening the design cycle. [ABSTRACT FROM AUTHOR]
– Name: AbstractSuppliedCopyright
  Label:
  Group: Ab
  Data: <i>Copyright of Journal of Intelligent Manufacturing is the property of Springer Nature and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.)
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      – SubjectFull: Process optimization
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      – SubjectFull: Design techniques
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      – SubjectFull: Bayesian analysis
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              Text: Jan2026
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