A scientific and technological project operation and maintenance risk assessment method and system based on big data analysis
By leveraging big data analytics, combined with multi-source data fusion and graph convolutional neural networks, the inaccuracy and complexity of risk assessment in the operation and maintenance of science and technology projects have been addressed. This has enabled real-time risk warnings and dynamic adjustments, thereby improving the efficiency and security of project management.
Patent Information
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Filing Date
- 2026-05-19
- Publication Date
- 2026-07-17
AI Technical Summary
In the current operation and maintenance management of science and technology projects, the reliance on manual monitoring and experience-based judgment leads to inaccurate and inefficient risk assessment. The processing of multi-source data is cumbersome and prone to errors, making it impossible to provide real-time early warnings and respond to complex project changes. Furthermore, there is a lack of application of deep learning and graph neural networks.
By employing big data analytics methods, through multi-source data fusion, variational autoencoders, and graph convolutional neural networks, we can achieve real-time data acquisition, preprocessing, and risk assessment, dynamically adjust risk thresholds, automatically identify potential risks, and formulate response strategies.
It enables accurate risk prediction and efficient response strategy formulation, improves the accuracy and speed of risk assessment, provides scientific and flexible risk control solutions, and ensures the smooth execution of projects.
Smart Images
Figure CN122414830A_ABST