Feedforward–Feedback Symmetry for Risk Control: A Hybrid Framework Coupling Genetic Algorithms with RAG-Enhanced Large Language Models

Petroleum engineering projects face complex risk environments. Existing risk control systems often only provide overall risk safety thresholds. They lack effective quantitative interval estimation methods. Moreover, translating quantitative analysis findings into actionable on-site management instructions remains challenging. Symmetry serves as a core analytical perspective for cutting-edge research in control theory and system engineering. Feedforward and feedback controls are functionally complementary and sequentially cascaded, featuring intrinsic complementary symmetry. From the perspective of feedforward–feedback symmetry, this study constructs a GA-LLM hybrid framework combining genetic algorithm (GA) and large language models (LLMs) to address the above shortcomings. This framework is jointly composed of four collaboratively functioning modules, comprising the risk status input module, GA feedforward control module, retrieval-augmented generation (RAG) knowledge retrieval module, and the LLM feedback control strategy-generation module. In this framework, the GA serves as the feedforward controller, which computes the joint inscribed control box for each risk factor offline based on the risk relationship model established by the Back Propagation (BP) neural network. The RAG-enhanced LLM serves as the feedback controller, dynamically generating structured risk control instructions based on deviations. Case validation results demonstrate that the BP neural network achieved excellent performance with an R2 of 0.99575 under leave-one-out cross-validation. The GA successfully solved the joint control box for the 14 risk factors, achieving a 100% joint constraint satisfaction rate for any combination within the box. The RAG retrieval module achieved a Recall@5 of 0.8933, MRR@5 of 0.7367, nDCG@5 of 0.7505, and Success@5 of 1.000. Ablation experiments show that the RAG-LLM scheme outperformed both the LLM without RAG scheme and the rule-based template scheme across four dimensions, with an inter-rater reliability ICC(2,1) of 0.719, reaching a moderate reliability level. This study integrates the quantitative optimization capability of GA with the semantic generation capability of LLM, enabling the transformation from the joint control box to executable management instructions. It not only provides a practical tool for petroleum engineering risk management but also offers new insights for the design of intelligent control systems from the perspective of feedforward–feedback symmetry.