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 se...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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KeAi Communications Co., Ltd.
2023-03-01
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| Series: | Journal of Safety Science and Resilience |
| Subjects: | |
| Online Access: | http://www.sciencedirect.com/science/article/pii/S2666449622000500 |
| _version_ | 1797867568833232896 |
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| author | Yudie Jianyao Qi Zhang Liang Ge Jianguo Chen |
| author_facet | Yudie Jianyao Qi Zhang Liang Ge Jianguo Chen |
| author_sort | Yudie Jianyao |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-04-09T23:42:15Z |
| format | Article |
| id | doaj.art-081a84d39a9e4ba9ade747e06bf6321d |
| institution | Directory Open Access Journal |
| issn | 2666-4496 |
| language | English |
| last_indexed | 2024-04-09T23:42:15Z |
| publishDate | 2023-03-01 |
| publisher | KeAi Communications Co., Ltd. |
| record_format | Article |
| series | Journal of Safety Science and Resilience |
| spelling | doaj.art-081a84d39a9e4ba9ade747e06bf6321d2023-03-18T04:42:50ZengKeAi Communications Co., Ltd.Journal of Safety Science and Resilience2666-44962023-03-01416174Technical methods of national security supervision: Grain storage security as an exampleYudie Jianyao0Qi Zhang1Liang Ge2Jianguo Chen3Department of Engineering Physics, Tsinghua University, Beijing 100084, China; Institute of Public Safety Research, Tsinghua University, Beijing 100084, China; Tsinghua Shenzhen International Graduate School, Tsinghua University, Shenzhen 518055, ChinaDepartment of Engineering Physics, Tsinghua University, Beijing 100084, China; Institute of Public Safety Research, Tsinghua University, Beijing 100084, China; Tsinghua Shenzhen International Graduate School, Tsinghua University, Shenzhen 518055, ChinaNational Food and Strategic Reserves Administration, Beijing 100084, ChinaDepartment of Engineering Physics, Tsinghua University, Beijing 100084, China; Institute of Public Safety Research, Tsinghua University, Beijing 100084, China; Corresponding author.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.http://www.sciencedirect.com/science/article/pii/S2666449622000500Grain storage securitySupervision modelAbnormal data mining |
| spellingShingle | Yudie Jianyao Qi Zhang Liang Ge Jianguo Chen Technical methods of national security supervision: Grain storage security as an example Journal of Safety Science and Resilience Grain storage security Supervision model Abnormal data mining |
| title | Technical methods of national security supervision: Grain storage security as an example |
| title_full | Technical methods of national security supervision: Grain storage security as an example |
| title_fullStr | Technical methods of national security supervision: Grain storage security as an example |
| title_full_unstemmed | Technical methods of national security supervision: Grain storage security as an example |
| title_short | Technical methods of national security supervision: Grain storage security as an example |
| title_sort | technical methods of national security supervision grain storage security as an example |
| topic | Grain storage security Supervision model Abnormal data mining |
| url | http://www.sciencedirect.com/science/article/pii/S2666449622000500 |
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