Research on the Mechanism of Computer Algorithms in Improving Production Efficiency in Intelligent Manufacturing

The dynamic response of complex soil under earthquake action is a key factor in triggering geological secondary disasters and engineering structural damage. Traditional continuous medium models are limited in the accuracy of disaster risk analysis due to difficulties in characterizing microscopic particle behavior, inefficient parameter calibration, and insufficient dynamic risk assessment. This article constructs a coupled framework of “discrete element simulation machine learning GIS analysis” to achieve cross scale quantification of complex soil seismic response and risk. Establish a particle model with fault and layer characteristics using discrete element method, and capture the evolution of pore water pressure and particle motion by combining fluid structure coupling algorithm; Using LSTM network to invert microscopic parameters (error $\leqslant \text{2. 1 \%}$) and random forest model to predict liquefaction probability (accuracy 91.3%); Integrate multi-source data and generate dynamic risk zoning through GIS (spatial overlap of 88%, response delay $\leqslant 8$ seconds). Experiments have shown that the framework reduces simulation errors by 65% and improves parameter inversion efficiency by 85% compared to traditional methods. It can provide scientific support for seismic design and emergency decision-making in complex geological areas, and is of great significance for enhancing earthquake disaster prevention and control capabilities.