## 1 Introduction  
With upgrades to software and hardware, the efficiency of pre-processing and solutions to simulations have continuously been improved upon. However, the post-processing data analysis of a simulation is still inseparable from the engineers' experience. As \"tacit knowledge\" [1], this kind of experience is difficult to describe and disseminate. When employees leave or change jobs, their experiences in analyzing post-processing data cannot be retained. In the absence of such \"experience\", it will be not easy for successors to succeed. Currently, many researchers are focusing on intelligent algorithms to make judgments or diagnoses in various fields. In the field of simulations related to mechanical design, [PERSON] has developed an automatic post-processing system with versatility and adaptability to analyze simulation result data based on a secondary development of Visual Studio and Python built-in ABAQUS, which greatly reduces the threshold for users to obtain finite element analysis reports [2]. [PERSON] built a system that combines the knowledge base and simulation post-processing of the threshing device in a combine harvester based on SQL Server 2012 and Visual Studio 2017 software, which solved the problems of being time-consuming, being cumbersome to operate, and having low efficiency for simulation post-processing [3]. [PERSON] integrated the MySQL database, Java language, B/S architecture, and other technologies to develop a flow field simulation post-processing and cleaning device optimization platform that includes a knowledge base management system, a simulation post-processing module, and a multi-objective optimization module [4]. [PERSON] et al. developed an automatic simulation and analysis system for sub-frames based on UG NX 12.0 and ANSYS 2018 software and fully automated the whole finite element analysis process from pre-processing and solution to post-processing in combination with the transformation, preservation, extraction, and acquisition of geometric features of the model [5]. In recent years, with the development and improvement of artificial intelligence, there have been many breakthroughs in key-point detection algorithms and reasoning mechanisms based on deep learning. [PERSON] et al. solved the problem of pedestrian image offset and attitude difference with Openpose, locating 18 key points, using attitude guidance and feature alignment to recognize people with different poses, thus retrieving the difference and dislocation of pedestrian images [6]. [PERSON] et al. established an improved YOLOv8-Pose model to detect the key points of the stem in the red ripen stage of strawberries, which could recognize the key points of the fruit stem in complex environments [7]. [PERSON] et al. proposed a keypoint detection method for weld feature extraction and designed a network model for weld feature extraction based on Convolutional Neural Networks (CNNs); the methods can improve the accuracy and adaptability of weld recognition and ensure the quality of automatic welding because of their high detection accuracy [8]. [PERSON] et al. proposed a training strategy to integrate a priori anatomical knowledge into cellular neural networks through a new regularization model that can be adapted to different analytical tasks (e.g., image enhancement and segmentation) and showed its applicability in multimodal cardiac datasets and public benchmarks [9]. In view of the many challenges faced by traditional human pose recognition methods, [PERSON] et al. introduced a lightweight ghost net module in the backbone part, an ACmix attention mechanism in the neck part, and a coordinate attention mechanism in the head part, which improved the loss function and confidence function and enhanced the stability of the model [10]. [PERSON] et al.