The invention relates to the technical field of quality supervision and early warning management, in particular to an AI-based subway
engineering construction quality supervision and early warning
management system and method, and the
system comprises a process data fusion module which obtains the sensor
monitoring data deviation degree, the material detection qualified rate and the personnel operation specification conformity of a
shield tunneling process. According to the method, a quality
vulnerability index for expressing
process state fluctuation is established by fusing a sensor
monitoring data deviation degree, a material detection qualification rate and a personnel operation specification conformity, and an accumulated
risk assessment mode based on a process chain conduction relation is formed by combining dynamic quantification of an upstream process defect on a downstream influence probability. And the capability of identifying the
coupling influence between the working procedures is enhanced. And synchronously analyzing information request
metadata, scheduling personnel hour data,
access control behavior records and rectification
cycle time, and constructing a multi-dimensional collaborative efficiency index sequence covering information circulation efficiency, personnel operation load and problem solving efficiency.