A data-driven management system for enterprise digital transformation

CN122285651APending Publication Date: 2026-06-26LINYI UNIVERSITY
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Patent Information

Authority / Receiving Office
CN · China
Patent Type
Applications(China)
Current Assignee / Owner
LINYI UNIVERSITY
Filing Date
2026-05-16
Publication Date
2026-06-26

AI Technical Summary

Technical Problem

Enterprises face problems such as data silos, poor data quality and inconsistent definitions, and a lack of intelligent analysis and autonomous decision-making capabilities during the digital transformation process. As a result, data cannot form a holistic insight, and decision response is slow and inaccurate.

Method used

It adopts a multi-source heterogeneous data access layer, a unified data asset management layer, an intelligent analysis and decision engine layer, and an automated execution linkage layer to achieve real-time data collection, standardized cleaning, intelligent analysis, and automated execution. Combined with natural language processing and machine learning models, it supports multi-agent collaboration and cross-system linkage.

Benefits of technology

It enables efficient data collection and analysis, shortens analysis response time to the second level, improves the discovery efficiency and utilization value of data assets, ensures that decisions can be promptly and effectively translated into business actions, and optimizes model parameters through closed-loop feedback.

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Abstract

This invention discloses a data-driven management system for enterprise digital transformation, applicable to various enterprise digital transformation scenarios, including but not limited to the construction of digital management platforms in industries such as manufacturing, retail, finance, and logistics. The intelligent diagnostic and decision support functions for manufacturing digital maturity provide manufacturing enterprises with a scientific, quantitative, and automated digital transformation navigation tool. Through the implementation of this invention, enterprises can streamline the entire process from multi-source data access to intelligent decision-making and automated execution, effectively addressing core pain points in digital transformation such as data silos, poor data quality, and delayed decision response. This significantly improves the enterprise's data utilization efficiency and decision-making intelligence level, demonstrating significant industrial practical value and broad application prospects.
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