PFC数值模拟所需的微观参数通常通过人工试算的方式进行标定，该方法受标定人员经验的影响，效率较低，难以快速处理大量岩石试件。以平行黏结模型为例，建立微观参数正交试验表并进行数值模拟，以此为样本分别使用支持向量回归机（SVR）和BP神经网络模型进行训练，对室内测得的宏观参数进行预测，得到的微观参数进行数值模拟分析预测效果，若效果不佳则将模拟数据加入样本继续训练直至获得理想的结果。研究表明：利用数值模拟和机器学习相结合的正反演方法，可以高效标定微观参数，其中BP神经网络模型需要试算7次，而支持向量机模型仅需试算3次，标定效率更高。因此，基于正反演结合的SVR微观参数辅助标定方法不仅效率高、可重复性强、不受标定人员经验影响，而且适用于批量试件的标定工作。

In numerical simulation studies utilizing the Particle Flow Code （PFC），the direct acquisition of microscopic parameters of discrete particles through experimental means presents a significant challenge.Traditionally，manual trial-and-error techniques are utilized，involving continuous adjustments of microscopic parameters to observe the corresponding effects on macroscopic mechanical parameters within the simulation.This iterative process is characterized by its randomness and lack of systematic approach，heavily reliant on the expertise of the calibrator，ultimately leading to diminished calibration efficiency and reproducibility.This challenge is particularly evident when calibrating microscopic parameters for extensive quantities of rock samples，requiring substantial manual labor and repetitive tasks.In order to mitigate these issues，a novel approach for calibrating microscopic parameters is essential，one that is not reliant on the calibrator’s skill level and ensures consistent reproducibility in the calibration of rock specimen parameters on a large scale.Utilizing the parallel bond model as a case study，an orthogonal experimental design table was constructed to investigate microscopic parameters，followed by numerical simulations to generate a comprehensive small-sample dataset.Support vector regression （SVR） and back propagation（BP） neural network models were separately trained on this dataset.This approach involves utilizing macroscopic parameters derived from PFC numerical simulations as the forward process，with machine learning techniques employed to predict microscopic parameters as the inverse process.If the prediction error for the macroscopic parameters measured in the laboratory is deemed inadequate，the corresponding microscopic parameters and their resultant macroscopic parameters are incorporated into the dataset for additional training until the desired outcome is attained.Studies have shown that utilizing machine learning with small-sample data，in conjunction with forward and inverse modeling，can effectively calibrate parameters.In particular，the BP model required 7 iterations，whereas the SVR model only needed 3 iterations to attain satisfactory outcomes，showcasing superior calibration efficiency.In scenarios involving numerous and highly nonlinear macro-micro parameters，the utilization of machine learning-assisted calibration presents notable benefits over traditional manual trial-and-error approaches，including enhanced efficiency，increased reproducibility，and improved generalizability.