Timely treatment and elimination of diseases and pests can effectively improve the yield and quality of crops, but the current identification methods are difficult to achieve efficient and accurate research and analysis of diseases and pests. To solve this problem, this study proposes a crop pest identification method based on a multilayer network model. First, the method provides a reliable sample dataset for the recognition model through image data enhancement and other operations; the corresponding pest image recognition and analysis model is constructed based on VGG16 and Inception-ResNet-v2 transfer learning network to ensure the completeness of the recognition and analysis model; then, using the idea of an integrated algorithm, the two improved CNN series pest image recognition and analysis models are effectively fused to improve the accuracy of the model for crop pest recognition and classification. The simulation analysis is realized based on the IDADP dataset. The experimental results show that the accuracy of the proposed method for pest identification is 97.71%, which improves the poor identification effect of the current method.

There are few studies on the land subsidence induced by shield tunneling in the water-rich sandy gravel stratum, which is of high research value. Linear regression and measured data were employed in this study to investigate the land subsidence induced by shield tunneling when crossing the water-rich sandy gravel stratum from Mudan Dadao Station to Longmen Dadao station of Luoyang Metro Line 2. The maximum land subsidence correction coefficient, α, and the settlement trough width correction coefficient, β, were introduced to modify the peck formula to predict land subsidence induced by shield tunneling in Luoyang’s water-rich sandy gravel stratum. It was discovered that the original Peck formula needs to be modified because its prediction result was significantly larger than the actual value. When the value ranges of α and β in the modified Peck formula were 0.379~0.690 and 0.455~0.508, respectively, the modified Peck formula presented a minor error, in terms of the prediction curve, compared with the original formula, and the prediction result was more reliable. The best prediction result could be obtained when α = 0.535 and β = 0.482. In addition, Python could effectively improve the calculation efficiency of the Peck formula modification.

Online higher education has become a steadily more popular way of learning for university students in the post-pandemic era. It has been emphasized that active learning and interactive communication are key factors in achieving effective performance in online learning. However, due to the lack of learning motivation of students and the lack of feedback data in online learning, there are numerous problems, such as the weak self-discipline of students, unsatisfactory learning experience, a high plagiarism rate of homework, and the low utilization of online teaching resources. In this study, an online homework intelligent platform implemented by information technology (IT) was proposed. It was based on the pedagogical self-regulated learning (SRL) strategy as a theoretical foundation, and information technology as a driver. Through setting online homework assignments, a sustainable means of promoting the four components of the SRL strategy, i.e., self-disciplinary control, independent thinking, reflective learning, and interest development, can be provided to university students. Therefore, this study explained the “4A” functions in the platform and analysed the details of their implementation and value, such as assistance in locating resources, assignment of differentiated homework, assessment of warning learning, and achievement of sharing. After three years of continuous improvements since COVID-19, this online platform has been successfully applied to students and teachers at our university and other pilot universities. A comparison of student teaching data, questionnaire responses and teacher interviews from the Computer Composition Principles course illustrated the sustainability as well as the effectiveness of the method.

With the rapid development of artificial intelligence, AI technology has been widely applied in various industries, including education. In English teaching, AI technology provides teachers with objective and instructive feedback, offers students a more personalized learning experience, helping them improve their English language skills, etc., and brings new development opportunities and challenges to English teaching as well. However, there are still some problems that need to be solved in college English teaching in our university. For this reason, based on the application of AI technology in college English teaching, this paper mainly explores the impact and application countermeasures of AI on college English teaching from the following four aspects: individuals with differences, teaching management, classroom teaching and teaching evaluation. In addition, the profound integration of college English education and AI will foster the advancement and transformation of college English instruction, ultimately serving as a catalyst for its reform.

The old fingerprint locks need to be operated on site, and there are great limitations; with the development of technology, various fingerprint locks with remote door opening have appeared, and if all of them are replaced, the cost is high, which will result in a certain amount of waste, so it is worthwhile to study the modification of the existing fingerprint locks to add the function of opening the door remotely. This paper proposes a method to increase the function of remote door opening by simple modification, which can well solve the above problems.