A cost monitoring and analysis platform for engineering projects supported by edge computing under the school-enterprise cooperation model

The design, execution, and evaluation of a cost monitoring and analysis platform, based on an Edge-Fog-Cloud Collaborative Computing (EFC-CCC) Framework, are presented in this research. To anticipate long-term cost efficiency, the suggested system integrates cloud-based predictive analytics, intermediate cost accumulation, and detection of anomalies at the fog layer, and distributed IoT-based data collection at the edge. A set of mathematical models is developed to evaluate cost variance, latency-sensitive cost efficiency, and alert prioritization. These models allow for the dynamic identification of budget changes under the variability of resource use over time. The platform is implemented utilizing data from construction projects, and under stable operating conditions, it is compared with typical edge-enabled baselines (YOLOX + GIoU + Soft-NMS + Model Pruning, EPOS-Fog, and BIM-VR Framework). The suggested architecture shows improved cost-tracking performance (94.5% normalized accuracy), lower communication overhead (1.3 Mbps), and reduced end-to-end latency (198 ± 6.4 ms). Additionally, the school-enterprise collaborative cycle is incorporated to improve deployment flexibility by refining the models using input.