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

Abstract

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.