Study on the method of identifying indicator diagram of sucker rod pump based on bionic pattern recognition

Sucker rod pump systems, accounting for over 80% of global artificial lift installations, face increasing challenges in maintaining operational efficiency due to complex working conditions and diverse fault patterns. Traditional monitoring methods rely heavily on manual interpretation, leading to inefficient maintenance and reduced production. This paper introduces an intelligent optimization approach for dynamometer card recognition in sucker rod pump systems based on biomimetic pattern recognition principles. The proposed method incorporates deep learning techniques with biological-inspired algorithms to achieve adaptive feature learning and pattern classification. By mimicking the hierarchical processing mechanisms of biological vision systems, our approach implements a multi-scale feature extraction framework that effectively captures both local and global pattern characteristics. A novel multi-objective optimization framework is developed to balance recognition accuracy and computational efficiency, integrating advanced attention mechanisms with adaptive decision-making strategies. Testing results from 2,500 dynamometer cards across various operating conditions demonstrate that the optimized system achieves 96.2% recognition accuracy while reducing computational time by 45% compared to traditional methods. The system maintains robust performance under diverse well conditions, with false alarm rates reduced to 1.8% and early fault detection capabilities improved by 65%. Field deployment in 20 oil wells validates the system's practical effectiveness, showing significant improvements in maintenance efficiency and production optimization. The proposed approach provides a promising solution for intelligent oil production management, offering potential applications in broader industrial monitoring scenarios.