The knowledge city and regional application of labour insurance supply management from big data algorithms

This paper investigated the effectiveness of labour insurance supply management in knowledge cities and regional applications through the use of big data (BD) algorithms. It compared the Big Data Algorithm-Based Management (BDAM) model with the traditional rule-based management (RBM) model across key metrics, including efficiency, safety, innovation, sustainability, costs, and resource utilisation. Results indicated that the BDAM model significantly outperformed the RBM model, with higher resource allocation efficiency (16% to 39% vs. 17.54% to 67.71%), superior safety levels (53.49% to 62.10% vs. 29.32% to 36.57%), and better innovation and sustainability scores. Although the BDAM model incurred higher initial costs, it demonstrated cost savings over time, with costs decreasing from 18.95 to 16.25, and maintained higher resource utilisation efficiency (0.737 to 0.810 vs. 0.573 to 0.635). The study emphasised the BDAM model's flexibility, scalability, and potential for integration with other smart city components.