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Taking precipitation processes at the edge of subtropical high as target objects, three typical sub-regions including the southern foothills of Taihang Mountains, the eastern foothills of Funiu Mountains, and the eastern plains in Huanghuai Region are investigated, according to the topography and precipitation distribution characteristics. On this basis, using multi-source fusion precipitation data of National Meteorological Information Center, the precipitation refinement verification index, FSS (fractional skill score) and STFSS (spatial temporal fractional skill score) that adds the temporal neighborhood are used to evaluate the forecasting performance of the precipitation diurnal variation of CMA-MESO and CMA-SH9. The results show that the precipitation frequency and intensity by CMA-MESO in mountainous areas are smaller than observations, leading to significant underestimation, while results by CMA-SH9 are both stronger. The deviation of precipitation amount forecast of CMA-MESO in plain area mainly comes from the deviation of precipitation frequency. The precipitation intensity forecast by CMA-SH9 is stronger than observations in the southern foothills of Taihang Mountains, and the forecast precipitation frequency in the afternoon is significantly larger. For the early morning to morning period in the eastern foothills of Funiu Mountains, the frequency of precipitation by CMA-MESO is significantly lower than observations, while the diurnal variation of precipitation intensity is better than that of CMA-SH9. For the eastern plains, the overestimation of precipitation in early evening by CMA-SH9 mainly comes from the significantly larger precipitation intensity, while CMA-MESO significantly underestimates the frequency of precipitation in early evening and early morning. Based on the analysis of FSS, CMA-MESO is superior to CMA-SH9 in hourly precipitation forecast of more than 10 mm during afternoon to mid-night in the eastern foothills of Funiu Mountains and 0200 BT to 0400 BT in the night in the eastern plains, but the conclusion is opposite in the southern foothills of Taihang Mountains in the nighttime. STFSS can better evaluate the temporal and spatial uncertainty of hourly precipitation forecasts. Precipitation forecasts of CMA-SH9 in southern foothills of Taihang Mountains from the afternoon to the first half of the night significantly lag behind observations, but its spatial-deviation-scale and performance is better than CMA-MESO. The precipitation forecast of CMA-MESO from 0200 BT to 0800 BT in the eastern foothills of Funiu Mountains is significantly ahead of observations, and the advance time is more obvious in the eastern plains in the morning.

With the continuous development and improvement of digital media technology, its position in dynamic visual design is also increasing. In this paper, we first combine LCEOPaCH algorithm to extract color features from visual images, use local energy pointing mode to get image color transformation information, optimize the dynamic visual measurement method based on dynamic binocular reconstruction, and construct a dynamic visual communication model combined with visual SLAM. Through the empirical data of the visual communication system, the localization accuracy of different methods in dynamic environments is compared based on the root mean square error value of absolute trajectory error in dynamic datasets A, B, and C. The quantization results of this paper’s algorithm under dynamic visual measurement analysis and color feature extraction data comparison in 6 sets of simulated images, the PSNR value is higher than 30, and the average value of PSNR is as high as 38.11 under 24 feature colors. The SSIM value for 8 feature colors is higher than 0.75 in the same quantization. The study demonstrates the advantages of digital media technology in visual communication design and provides practical design ideas.

The internal leakage fault-tolerant control problem of the electro-hydraulic servo actuator under the influence of multiple unmodeled dynamics is investigated in this paper, and an adaptive fault-tolerant control scheme based on unmodeled dynamics estimation and compensation is proposed. The model of the actuator is divided into two subsystems, which extended-state observers are respectively constructed to estimate the matched and mismatched unmodeled dynamics. Combined with the estimation results of the unmodeled dynamics, an adaptive fault-tolerant controller is designed by using the backstepping method. In which a controller reconfiguration mechanism based on internal leakage fault parameter online adaptation is used to accommodate the fault, and a feedforward compensation strategy is used to suppress the influence of unmodeled dynamics. Semi-physical simulation test of the proposed scheme is conducted under serious cylinder internal leakage. The test result shows that when the maximum internal leakage flow reaches 10.53 L/min, accounting for about 56.77% of the load flow, at the moment the opening of the servovalve is close to the maximum and the proposed scheme can still achieve high-precision position tracking control, where the maximum position tracking errors of fault transient and post-fault steady state are both limited within ±1.5% of the given position.

Squall line often leads to heavy rain, gale and hail, which is a difficult key problem in nowcasting. In order to explore the feasibility of deep learning for squall line identification, the training, validation and test set sample sets are established based on the radar data of Zhengzhou and Zhumadian in Henan Province during 2008-2020. The convolutional neural network (CNN) algorithm is used to construct a squall line identification model. The critical success index (CSI), equitable threat score (ETS), hit rate (POD) and false positive rate (FAR) are used to quantitatively evaluate the identification effect of the model. The influence of different sample composition and network structure on squall line identification effect are compared. The results show that the composition ratio of sample is imbalanced, because squall line accounts for very small proportion in all kinds of weather processes. This imbalance will degrade the classification performance of the identification model to squall line samples. The imbalance of sample composition can be improved by changing sampling mode and optimizing network structure, both can improve the identification efficiency, especially the latter. However, the combination of the two methods does not bring further improvement. The over fitting problem in network training can be alleviated by increasing the sparsity and randomness of the network structure. The validation set shows that CSI is 0.87, ETS is 0.82, POD is 0.96, and FAR is 0.10. Based on the test set, the echo can be correctly identified by network as non-squall line in the weak stage of convection development, and as squall line in the strong stage of squall line development. The echo intensity and spatial distribution of squall line cases differ greatly, and the samples in the test set have the image features which are not included in the training set, and therefore the identification effect reduces. The test set show that CSI is 0.66, ETS is 0.58, POD is 0.86, and FAR is 0.24. The research reveals that CNN can extract and learn the image features of squall line echo, and it has a certain ability to identify squall line.