Aiming at the deficiencies of the PLC teaching and training platforms in various colleges and universities, in order to meet the needs of students to debug PLC programs in various application places, taking elevator control as an example, this paper uses virtual reality technology, visualization technology, and computer programming technology to design a virtual simulation teaching platform based on PLC elevator control. Students can run the virtual simulation platform on the web page, learn the relevant basic knowledge, understand the operating principle of the elevator, and use the ladder diagram language to program in order to achieve the purpose of learning PLC. The results show that students can write and verify PLC programs anytime and anywhere, which greatly reduces the cost of purchasing training equipment and improves learning efficiency.

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To improve the accuracy of fuel injection in high-pressure common rail systems of diesel engine, a mathematical model based on differential equations was established in view of the process of fuel entering and ejecting the common rail pipe, and the pressure variation law under working condition in the common rail tube was simulated in this study. Aiming at reducing the fuel pressure fluctuations in the system, the optimal angular velocity of the cam in high-pressure pump is solved by binary search algorithm, and the optimal control strategies for maintaining the dynamic and stability of pressure are given as well. Numerical simulation results show that with the presented strategies precise control of the pressure can be achieved in the common rail system, helping to improve the efficiency of the diesel engines.

Accurate traffic flow forecasting is very important for urban planning and traffic management. However, this is a huge challenge due to the complex spatial–temporal relationships. Although the existing methods have researched spatial–temporal relationships, they neglect the long periodic aspects of traffic flow data, and thus cannot attain a satisfactory result. In this paper, we propose a novel model Attention-Based Spatial–Temporal Convolution Gated Recurrent Unit (ASTCG) to solve the traffic flow forecasting problem. ASTCG has two core components: the multi-input module and the STA-ConvGru module. Based on the cyclical nature of traffic flow data, the data input to the multi-input module are divided into three parts, near-neighbor data, daily-periodic data, and weekly-periodic data, thus enabling the model to better capture the time dependence. The STA-ConvGru module, formed by CNN, GRU, and attention mechanism, can capture both temporal and spatial dependencies of traffic flow. We evaluate our proposed model using real-world datasets and experiments show that the ASTCG model outperforms the state-of-the-art model.