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

作     者：Yudie Jianyao Qi Zhang Liang Ge Jianguo Chen 

作者机构：Department of Engineering PhysicsTsinghua UniversityBeijing 100084China Institute of Public Safety ResearchTsinghua UniversityBeijing 100084China Tsinghua Shenzhen International Graduate SchoolTsinghua UniversityShenzhen 518055China National Food and Strategic Reserves AdministrationBeijing 100084China 

出 版 物：《Journal of Safety Science and Resilience》 (安全科学与韧性（英文）)

年 卷 期：2023年第4卷第1期

页      面：61-74页

核心收录：

学科分类：120301[管理学-农业经济管理] 12[管理学] 1203[管理学-农林经济管理] 08[工学] 0837[工学-安全科学与工程] 

主　　题：Grain storage security Supervision model Abnormal data mining 

摘      要：Grain security guarantees national *** has many widely distributed grain depots to supervise grain storage ***,this has led to a lack of regulatory capacity and *** the development of reserve-level information technology,big data supervision of grain storage security should be *** 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 *** 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,*** abnormal data were filtered out by GBR combined with residuals as an *** 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 *** is contrary to the original intention of finding as many anomalies as possible for supervisory *** 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 *** on the existing grain information system,this paper provides a supervision model for grain storage that can help mine abnormal *** the current post-event supervision model,this study proposes a pre-event supervision *** study provides a framework of ideas for subsequent scholarly research;the addition of big data technology will help improve efficien