## 1 Introduction  
In recent years, as one of the crucial pieces of equipment in the field of oil extraction, the pumping unit has played a key role in improving oil field recovery rates and enhancing production efficiency. To ensure the smooth operation of the pumping unit and minimize the risk of equipment failures, accurate and timely diagnosis is of paramount importance. Dynagraph cards, as a commonly used diagnostic tool, provide valuable information about the operating condition and performance of the pumping unit. However, traditional methods for diagnosing pumping unit dynagraph cards are limited by manual analysis and domain expertise. Manual analysis requires a significant amount of time and effort, and is prone to subjective biases, leading to inconsistent and inaccurate diagnostic results [1]. In recent years, there have been remarkable advancements in the field of diagnosing pumping unit paragraph cards, primarily through the adoption of machine learning techniques. These methods have been developed to address the challenges associated with manual analysis and the classification of dynagraph cards. As a result, machine-learning-based approaches have shown great promise in improving the accuracy, efficiency, and objectivity of dynagraph card diagnosis. The development of dynagraph card diagnostic methods can be summarized as follows. Initially, expert diagnosis played a crucial role in dynagraph card diagnostics, relying on the extensive work experience and domain knowledge of oilfield engineering experts. By manually analyzing and interpreting dynagraph cards, experts could diagnose pump failures and provide corresponding solutions. This method relied on experts' accurate understanding of dynagraph card features and patterns to infer pump issues. With the rise of machine learning, statistical and pattern-recognition-based machine learning methods were applied to pump failure diagnosis. [PERSON] proposed a fault detection method employing support vector machines (SVM) and genetic algorithm optimization for accurate fault diagnosis based on pump fault information in 2007 [2]. Their method demonstrated high feasibility and effectiveness through experimental results. [PERSON] suggested a curve moments and PSO-SVM method to diagnose downhole conditions of pumping wells, achieving automated identification, feature parameter extraction, and pattern classification from dynagraph cards in 2013 [3]. Their approach exhibited excellent classification performance. With the advancement of deep learning, significant breakthroughs have been made in dynagraph card diagnostics. He presented a combination of convolutional neural networks (CNN) and long short-term memory (LSTM) networks for diagnosing gradual faults in 2019 [4]. By extracting multi-level abstract features and employing LSTM for sequence recognition, the method surpassed traditional mathematical models in diagnostic accuracy. Furthermore, the application of CNN-based image recognition techniques by [PERSON] enabled the diagnosis of oil well pumping unit failures through power card curve analysis [5]. This method showed high accuracy and practicality, making it a feasible approach for leveraging oilfield data assets. Despite the improvements in deep learning models, the small sample characteristics of dynagraph card data presented challenges when using large models like AlexNet and VGG in 2019. To address this, [PERSON] proposed an automatic recognition method based on transfer learning and support vector machines (SVM), utilizing a large amount of collected dynagraph card data from sensors for more efficient pumping system operating state recognition in 2020 [6]. Representative features of dynagraph cards were automatically extracted using transfer learning based on AlexNet. Combined with the extracted features, an SVM method based on error-correcting output codes (ECOC) was designed to identify the operating states of the pumping system, enhancing the accuracy and efficiency of fault diagnosis.