For the traditional laboratory manual management, there is a need for laboratory managers. The managers cannot keep abreast of the laboratory conditions, and forget to shut down and close the windows, causing waste of electricity and equipment damage. Based on NB-IoT and artificial intelligence, a laser intelligent management system is developed using technologies such as network communication, intranet, automatic control, artificial intelligence, and software. Through this system, administrators can grasp the situation of the laboratory in real time, realize the intelligent management of the laboratory, improve the scientific management of the laboratory, improve the work efficiency of laboratory technicians, and better play the laboratory as an important platform for scientific research training in colleges and universities. This paper aims to develop an intelligent laboratory management system based on NB-IoT and AI technology. Through this system, the manager can grasp the situation of the laboratory in real time, realize the intelligent management of the laboratory, and better play the role of the laboratory as an important platform for scientific research and talent training.

Experimental instructional design is an important pedagogical component of university teaching and learning, an important means of cultivating students’ innovative spirit and practical skills, and has an important status and role that cannot be replaced by any other means of teaching and learning. Assessment for learning as learning, assessment for learning, and assessment as learning are three paradigms of educational assessment that complement each other in achieving curricular and pedagogical goals and together form learning-based assessment. As an important component of national science and technology development, measuring the effectiveness of laboratory instructional design in universities and research institutions is of special significance. This paper presents the authors’ research on the background, evaluation characteristics, evaluation content, and methods of experimental teaching evaluation in the information technology environment, with examples of their application.

This study proposes the first fully deep learning-based structural response intelligent computing framework for civil engineering. For the first time, from the data side to the model side, the structural information of the structure itself and any loading system is comprehensively considered, which can be applied to materials, components, and even structures, system and other multi-level mechanical response prediction problems. First, according to the characteristics of structural calculation scenarios, a unified data interface mode for structural static characteristics is formulated, which preserves the original structural information input and effectively reduces manual intervention. On this basis, an attention mechanism and a deep cross network are introduced, and a structural static feature representation learning model PADCN is proposed, which can take into account the memory and generalization of structural static features, and mine the coupling relationship of different structural information. Then, the PADCN model is integrated with the dynamic feature prediction model Mechformer and connected with the designed general data interface to form an end-to-end data-driven structural response intelligent computing framework. In order to verify the validity of the framework, numerical experiments were carried out with the steel plate shear wall structure as the carrier, in which a data augmentation algorithm suitable for the field of structural calculation was proposed to alleviate the problem of lack of structural engineering data. The results show that the deep learning model based on this framework successfully predicts the whole-process nonlinear response of specimens with different structures, the simulation accuracy is better than that of the fine finite element model, and the computational efficiency exceeds the traditional numerical method by more than 1000 times, achieving a qualitative improvement. It is proven that the intelligent computing framework has excellent accuracy and efficiency.

The separation of time and space in immersive virtual teaching makes students unable to realize emotional communication, which may affect students’ mental health. In recent years, the use of affective computing technology to solve the problem of affective loss in distance education has become a key research topic. In order to realize the problem of emotion interaction in immersive virtual teaching, a semisupervised support vector machine- (SVM-) based affective interaction model was proposed. First, the natural language sequences of students in the virtual teaching environment are preprocessed using a statistical-based framing method, and mutual information and expected cross-entropy are used as feature selection methods. Then, a vector space model based on TF/IDF feature term weights is proposed to implement the feature vector representation of natural language sequences. Finally, after the constructed sentiment space, a semisupervised SVM is employed as the classifier to complete the affective interaction computation. The experimental results of emotion classification show that the proposed model is able to determine and understand the emotional state more accurately than other traditional models and significantly improves the training speed. In addition, the proposed model can provide emotional encouragement or emotional compensation according to the specific emotional state of the learner.