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This research was aimed at exploring the application value of a mobile medical management system based on Internet of Things technology and medical data collection in stroke disease prevention and rehabilitation nursing. In this study, on the basis of radio frequency identification (RFID) technology, the signals collected by the sensor were filtered by the optimized median filtering algorithm, and a rehabilitation nursing evaluation model was established based on the backpropagation (BP) neural network. The performance of the medical management system was verified in 32 rehabilitation patients with hemiplegia after stroke and 6 healthy medical staff in the rehabilitation medical center of the hospital. The results showed that the mean square error (MSE) and peak signal-to-noise ratio (PSNR) of the median filtering algorithm after optimization were significantly higher than those before optimization ( P < 0.05 ). When the number of neurons was 23, the prediction accuracy of the test set reached a maximum of 89.83%. Using traingda as the training function, the model had the lowest training time and root mean squared error (RMSE) value of 2.5 s and 0.29, respectively, which were significantly lower than the traingd and traingdm functions ( P < 0.01 ). The error percentage and RMSE of the model reached a minimum of 7.56% and 0.25, respectively, when the transfer functions of both the hidden and input layers were tansig. The prediction accuracy in stages III~VI was 90.63%. It indicated that the mobile medical management system established based on Internet of Things technology and medical data collection has certain application value for the prevention and rehabilitation nursing of stroke patients, which provides a new idea for the diagnosis, treatment, and rehabilitation of stroke patients.

We used the World Food Studies (WOFOST) model to analyze meteorological, soil, and winter wheat (Triticum aestivum L.) growth period data in Xuzhou, Huaian, and Changzhou, Jiangsu Province from 2008 to 2017. The data collected were used to study the effects of warming and drought on winter wheat yield and dry matter accumulation. The simulation results showed that single warming stress (1, 2, and 3 ℃), drought stress (mild, moderate, and severe), and combined drought and warming stress can reduce the yield of winter wheat. The highest yield reduction rate was 25.5% in Changzhou under the condition of single warming, and the yield reduction rate increased by 5% when the soil relative humidity decreased by 10% under the condition of single drought. The grain dry matter formation of winter wheat was also inhibited by warming and drought, and the lowest dry matter ratio was only 42.4% in Changzhou under drought. The influence of combined stress was greater than that of single stress under various contrasts. However, in the middle of Jiangsu Province, the yield of winter wheat was most affected by drought, and the distribution of dry matter accumulation decreased from south to north. The yield reduction of winter wheat under other stresses increased from south to north, and the dry matter distribution of grain was lower in the south than in the north and lower in the central part of Jiangsu Province.

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