Technical methods of national security supervision:Grain storage security as an example

Grain security guarantees national security.China has many widely distributed grain depots to supervise grain storage security.However,this has led to a lack of regulatory capacity and manpower.Amid the development of reserve-level information technology,big data supervision of grain storage security should be improved.This study proposes big data research architecture and an analysis model for grain storage security;as an example,it illustrates the supervision of the grain loss problem in storage security.The statistical analysis model and the prediction and clustering-based model for grain loss supervision were used to mine abnormal data.A combination of feature extraction and feature selection reduction methods were chosen for dimensionality.A comparative analysis showed that the nonlinear prediction model performed better on the grain loss data set,with R2 of 87.21%,87.83%,91.97%,and 89.40%for Gradient Boosting Regressor(GBR),Random Forest,Decision Tree,XGBoost regression on test sets,respectively.Nineteen abnormal data were filtered out by GBR combined with residuals as an example.The deep learning model had the best performance on the mean absolute error,with an R2 of 85.14%on the test set and only one abnormal data identified.This is contrary to the original intention of finding as many anomalies as possible for supervisory purposes.Five classes were generated using principal component analysis dimensionality reduction combined with Density-Based Spatial Clustering of Applications with Noise(DBSCAN)clustering,with 11 anomalous data points screened by adding the amount of normalized grain loss.Based on the existing grain information system,this paper provides a supervision model for grain storage that can help mine abnormal data.Unlike the current post-event supervision model,this study proposes a pre-event supervision model.This study provides a framework of ideas for subsequent scholarly research;the addition of big data technology will help improve efficient supervisory capacity in the field of grain supervision.

Reduced Order Modeling & Controller Design for Mass Transfer in a Grain Storage System

This paper considers the problem of simulating the humidity distributions of a grain storage system. The distributions are described by partial differential equations(PDE). It is quite difficult to obtain the humidity profiles from the PDE model. Hence, a discretization method is applied to obtain an equivalent ordinary differential equation model. However, after applying the discretization technique, the cost of solving the system increases as the size increases to a few thousands. It may be noted that after discretization,the degree of freedom of the system remain the same while the order increases. The large dynamic model is reduced using a proper orthogonal decomposition based technique and an equivalent model but of much reduced size is obtained. A controller based on optimal control theory is designed to obtain an input such that the output humidity reaches a desired profile and also its stability is analyzed.Numerical results are presented to show the validity of the reduced model and possible further extensions are identified.

全国秋粮旺季收购进入集中上量阶段

目前全国秋粮旺季收购进入集中上量阶段,截至12月15日,主产区粮食企业累计收购秋粮超过9000万吨,同比增加近500万吨,粮食收购平稳有序。党中央、国务院高度重视粮食安全,习近平总书记多次强调中国人的饭碗任何时候都要牢牢端在自己手中,饭碗主要装中国粮。

Experiment and simulation research of storage for small grain steel silo

Knowing the temperature distribution in silo is a convenient and efficient way to control the process of grain storage.A three-dimensional(3-D)numerical model was used to study the temperature variation in small grain steel silo under quasi-steady state.In this study,experiments were conducted and porous media model was adopted.Results of numerical simulation and experiment were compared and the results indicated that grain temperature was influenced by temperature of the wall,grain stacking height,and the distance between grain and wall.The higher the wall temperature,the more the temperature increases.If the wall temperature is low,the effect of wall temperature on temperature distribution is significant.The temperature at the top part of grain varied obviously with the changes of temperature in air layer.Overall,numerical simulation results coincided with experimental results and the model established in this study is valuable for predicting grain temperature in steel silo.

Insecticidal efficacy of three vegetable oils as post-harvest grain protectants of stored wheat against Rhyzopertha dominica （F.） （Coleoptera： Bostrychidae）

The lesser grain borer, Rhyzopertha dominica is a major insect pests of stored grain in the tropics. Vegetable oils （chamomile, sweet almond and coconut） at 2.5, 3.5, 5.0, 7.0 and 10.0 mL/kg were tested against Rhyzopertha dominica （F.） in wheat grain. All bioassays were conductr, d at 30℃ and 65% ＋ 2% RH. Treatments with vegetable oils at high dose （10.0 mL/kg） achieved over 95% control within 24 h of exposure to freshly treated grain, There was little difference between the three oils in their effect. Persistence of oils in grains was tested at short-term storage intervals （48, 72 and 96 h） and intermediate-term intervals （10, 20 and 30 days） after treatments. The activity of all products decreased with storage period. Seed viability was reduced by the high dose rate （10.0 mL/kg） of oil treatments. The potential use of vegetable oils as supplementary or alternative grain protectants against insect damage in traditional grain storage in developing countries is discussed.