A Cross-Regional Cost Collaborative Optimization Model Based on a BIM Lightweight Engine and Mobile Edge Computing

With the rapid development of building information modeling (BIM) technology, its application in construction projects has become widespread, particularly in the collaborative work during the design, construction, and operation phases. However, traditional BIM models, due to their large data volume and complex structure, present significant challenges in data transmission and real-time processing during cross-regional collaboration. Meanwhile, mobile edge computing (MEC), as an emerging computational paradigm, reduces data transmission latency and enhances processing efficiency by deploying computing and storage resources at the network edge. The integration of a BIM lightweight engine and MEC offers a more efficient solution for cross-regional cost collaboration. While previous studies have explored the combination of BIM with cloud and edge computing, most have focused on single-region scenarios, with the lightweight methods for BIM data and MEC deployment for cross-regional collaboration still lacking efficiency and practicality. Furthermore, existing optimization algorithms often fail to fully address the unique demands of cross-regional collaboration, leading to suboptimal overall performance. To address these issues, a cross-regional cost collaborative optimization model based on the BIM lightweight engine and MEC was proposed in this study. The main content of this study includes a) the design of a BIM lightweight engine based on point cloud compression, which improves data transmission efficiency through efficient compression techniques; b) the investigation of the MEC server deployment strategy in cross-regional cost collaborative scenarios, optimizing the distribution and scheduling of computational resources; and c) the development of a cross-regional cost collaborative optimization method based on an improved genetic algorithm to enhance the intelligence and efficiency of resource scheduling. This research provides not only a new theoretical framework for the integration of BIM and MEC but also technical support for cross-regional collaborative optimization in practical engineering.