IDEAS home Printed from https://ideas.repec.org/a/ids/ijisde/v19y2025i1p94-106.html
   My bibliography  Save this article

Risk warning method for the whole process of production project based on multi-source data mining

Author

Listed:
  • Ya Zhou
  • Mingjie Zhang
  • Shaohuan Cheng
  • Xinli Luo
  • Jiefeng Wen
  • Jinlei Hu

Abstract

Aiming at the problem that the correlation coefficient between the final screening early warning indicators is small in the whole process of risk index screening of production projects, a risk early warning method for the whole process of production projects based on multi-source data mining is proposed. The multi-source data mining technology is used to extract the potential hidden characteristics of risk indicators in the whole process of production projects, and the hidden characteristic relationship between multi-risk indicators is obtained, and the risk management index system of the whole process of production projects is established. Risk warning of the whole process of production projects. The test results show that the proposed risk early warning method has a larger cross-correlation coefficient calculated in each index group, which verifies that the early warning method has high application reliability, can obtain more accurate data, and improve the early warning accuracy.

Suggested Citation

  • Ya Zhou & Mingjie Zhang & Shaohuan Cheng & Xinli Luo & Jiefeng Wen & Jinlei Hu, 2025. "Risk warning method for the whole process of production project based on multi-source data mining," International Journal of Innovation and Sustainable Development, Inderscience Enterprises Ltd, vol. 19(1), pages 94-106.
    • Handle: RePEc:ids:ijisde:v:19:y:2025:i:1:p:94-106
    as

    Download full text from publisher

    File URL: http://www.inderscience.com/link.php?id=143053
    Download Restriction: Access to full text is restricted to subscribers.
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:ids:ijisde:v:19:y:2025:i:1:p:94-106. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    We have no bibliographic references for this item. You can help adding them by using this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Sarah Parker (email available below). General contact details of provider: http://www.inderscience.com/browse/index.php?journalID=33 .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.