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In view of the drawback of traditional deterministic prediction that it cannot provide uncertainty information, this study proposed a prediction model that integrates point estimation and interval estimation, and innovatively applied it to the field of food safety risk pre-warning. In the point estimation, wavelet packet decomposition (WPD) was used to decompose the weekly risk level sequence and the autoregressive integrated moving average (ARIMA) model was used for prediction. In the interval estimation, the generalized autoregressive conditional heteroskedastic (GARCH) model was used to predict the residual. The WPD-ARIMA-GARCH model established in this study was applied to the safety risk prediction of soy sauce and pot-roast meat products from a certain region. The results showed that the safety risk of soy sauce and pot-roast meat products from this region was relatively high at the end of March and July in 2019, which was consistent with the actual situation. Meanwhile, in the risk prediction of soy sauce and pot-roast meat products from 10 different regions, the mean square error, mean absolute error, and mean absolute percentage error of the model were 1.626, 0.806, and 20.824, respectively, and the prediction interval normalized average and coverage width-based criterion values at the 90% confidence interval were both 0.024, which could cover all true values. Therefore, the model has high prediction accuracy and low error, is useful for risk control for the quality and safety of soy sauce and pot-roast meat products, and provide technical support for daily food safety supervision.

In recent years,deep learning has been widely used in image super-resolution reconstruction.To solve the problems of inadequate feature extraction,loss of details and gradient disappearance in super-resolution reconstruction methods based on deep learning,a deep recursive residual neural network model based on channel attention is proposed for single image super-resolution reconstruction.The proposed model constructs a simple recursive residual network structure by residual nested networks and jump connections to deepen the network and speed up its convergence while avoiding network degradation and gradient problems.An attention mechanism is introduced into the feature extraction part to improve the discriminant learning ability of the network for more accurate and more effective extraction of deep residual features,which is combined with the subsequent reconstruction network with parallel mapping structure to ensure the final accurate reconstruction.Quantitative and qualitative assessments are performed on benchmark dataset Set5,Set14,B100 and Urban100 at the magnification of 2,3 and 4 times by comparison with the mainstream methods.Experimental results show that the objective index values of the proposed method increase significantly compared to the comparative methods on all four test data sets.Among them,compared with the interpolation method and the SRCNN algorithm,the average PSNR improves 3.965dB and 1.56dB,3.19dB and 1.42dB,2.79dB and 1.32dB,respectively,at the magnification of 2,3 and 4 times.Visual effects show that the proposed method can recover image details better.