In order to improve the effect of higher English teaching, this paper builds a higher English education system based on Internet of Things technology. The optimization aim of this study is to provide an online DRX parameter optimization technique that is compatible with the RRC protocol architecture. This work employs algorithms based on reinforcement learning to accomplish parameter optimization and dynamic control by selecting optimal DRX parameters and dynamically configuring them via decision-making processes. Furthermore, this article enhances the Internet of Things algorithm and applies it to the higher English education system that this study constructs. Furthermore, this study combines the real demands to develop the functional structure module of the higher English education system, constructs the higher English education system based on the Internet of Things system, and tests the performance of the system constructed in this paper. Finally, the system structure performance verification is carried out through mathematical statistics. The research results show that the system constructed in this paper has a good teaching effect.

With the development of information technology, precision agriculture has also ushered in new development prospects. The use of farm robots to accurately identify the navigation path is of great significance for achieving accurate positioning of agriculture. In this study, when analysing the extraction algorithm of farm robot visual navigation based on dark primary colours, a method for pre-processing and edge detection of farmland images based on dark primary colours is proposed. At the same time, the least square method of linear fitting is used for the navigation path of agricultural robot, and then the fitting program is executed. On this basis, combined with the actual situation of outdoor farming and greenhouse cultivation of crops, the effectiveness of the robotic visual navigation extraction algorithm was verified. The research results show that for any form of farmland cultivation, image extraction technology based on dark primary colours can effectively distinguish between soil and crops, and the visual navigation path of farm robots fitted with least squares is basically linear, which is consistent with the commonly used crops for farm planting. The legal route is basically the same, and then the effectiveness of the extraction algorithm is verified. It is hoped that this study will provide a certain reference and reference for the analysis of the field navigation robot visual navigation extraction algorithm based on dark primary colours.

In the development of modern logistics, the role of automated cargo warehousing is gradually reflected, which is essential for the automatic distribution of goods. This paper briefly introduced the automatic location allocation model and the particle swarm optimization (PSO) algorithm used to optimize the model. At the same time, it introduced the concept of genetic operator and multi-group co-evolution to improve the algorithm, and then the simulation analysis of standard PSO and improved PSO was performed on MATLAB software. The results showed that the improved PSO iterated fewer times and get better solution sets; compared with the manual allocation scheme, the improved PSO calculation reduced more warehousing time, lowered more center of gravity height, and improved shelf stability. In summary, the improved PSO algorithm can effectively optimize the automated goods dynamic allocation and warehousing model.