As the core of urban infrastructure

municipal engineering faces issues such as subjective planning

static resource allocation

and insufficient coordination in terms of progress and resource management. This paper

centered on AI algorithms

constructs a dynamic resource allocation strategy: by integrating historical

real-time

and external data through the LSTM model

it achieves precise daily demand prediction for resources (e.g.

MAE for concrete usage is only 5%); It designs a "real-time collection - deviation analysis - collaborative allocation" mechanism

relying on IoT and 5G to obtain real-time data on equipment and progress

and dynamically adjust resources based on deviation types; And it formulates emergency plans for scenarios such as supply delays and equipment failures. This strategy addresses the pain points of traditional management

enhances resource utilization

and provides a feasible path for the transformation of municipal engineering management from experience-driven to data-driven.