The analysis of the quality indexes of sugarcane juice plays a vital role in the process of refining sugarcane, breeding, cultivation, and production management. The paper analyzes the dynamic laws of five quality indexes (i.e., brix, purity, polarization, sucrose content, and reducing sugar) combined with graphs over time along the course of crushing season (December-March) in Guangxi province of China. During this time, the sugarcane is in the mature stage and hypermature stage. At the beginning of December to early January, during which sugarcane is in the later stage of maturity, the nutrients are accumulating, causing brix, purity, polarization, and sucrose content increase. At the beginning of January to mid-February, due to low temperature and insufficient light, it is not conducive to accumulation of nutrients. However, there is the so-called "sugar back" phenomenon and reducing sugar rises gradually in March, leading to deterioration of the quality of sugarcane juice. The results show that timely harvest of sugarcane is beneficial for sugar making. The regression analysis results show that some of quality indexes have strong correlation between them and the regression models are extremely significant, indicating that the prediction results are ideal.

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The existence of high proportional distributed energy resources in energy Internet (EI) scenarios has a strong impact on the power supply-demand balance of the EI system. Decision-making optimization research that focuses on the transient voltage stability is of great significance for maintaining effective and safe operation of the EI. Within a typical EI scenario, this paper conducts a study of transient voltage stability analysis based on convolutional neural networks. Based on the judgment of transient voltage stability, a reactive power compensation decision optimization algorithm via deep reinforcement learning approach is proposed. In this sense, the following targets are achieved: the efficiency of decision-making is greatly improved, risks are identified in advance, and decisions are made in time. Simulations show the effectiveness of our proposed method.

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