Liu Dai, He Chengxiang, Ma Jianlong
Institute of Chengdu-Chongqing Economic Zone Development, Chongqing Technology and Business University, Chongqing, China.
School of Logistics Engineering, Chongqing Finance and Economics College, Chongqing, China.
Sci Rep. 2025 Jan 28;15(1):3581. doi: 10.1038/s41598-025-88085-2.
Scientific prediction of migrant worker numbers provides decision-making references for resolving rural talent supply issues. Based on the evolutionary patterns and data features of Chongqing's migrant workers, a new grey prediction model is constructed. The new model is constructed by introducing fractional-order operators in the real domain. In this way, the accumulating order of the traditional N_Verhulst model is optimized. It expands from 1 to all real numbers, thus enhancing its capacity to mine approximately saturated S-shaped time-series data. When the new N_Verhulst model is applied to simulate and predict migrant worker numbers, after optimizing the accumulating order, the mean relative simulation percentage error of the N_Verhulst model reduces from 3.66 to 2.93%, the mean relative forecasting percentage error from 8.02 to 2.18%, and the comprehensive mean relative percentage error from 4.53 to 2.78%. This shows that the optimization boosts the simulation and prediction performance of the N_Verhulst model. The prediction results show that the number of migrant workers in Chongqing will experience an orderly growth, rising from 2.41 million in 2023 to 2.85 million in 2028, with an increase of 18.26% and an average annual growth rate of 3.41%.
农民工数量的科学预测为解决农村人才供给问题提供决策参考。基于重庆农民工的演变模式和数据特征，构建了一种新的灰色预测模型。该新模型通过在实数域引入分数阶算子来构建。通过这种方式，优化了传统N_Verhulst模型的累加阶数。使其从1扩展到所有实数，从而增强了其挖掘近似饱和S形时间序列数据的能力。当将新的N_Verhulst模型应用于模拟和预测农民工数量时，在优化累加阶数后，N_Verhulst模型的平均相对模拟百分比误差从3.66%降至2.93%，平均相对预测百分比误差从8.02%降至2.18%，综合平均相对百分比误差从4.53%降至2.78%。这表明优化提高了N_Verhulst模型的模拟和预测性能。预测结果表明，重庆农民工数量将有序增长，从2023年的241万增长到2028年的285万，增长18.26%，年均增长率为3.41%。
