ABSTRACT

Automatic detection of granary storage states is an important technology to ensure national food security. In view of distribution characteristics of bottom pressure, this study theoretically explored a relationship between detected value of bottom pressure of granaries and granary storage states through the technologies, such as integrated learning and nonlinear regression. On this basis, this research built layout models of single-ring pressure sensors on the bottom of granaries and proposed a detection method for granary storage states based on domain knowledge of statistics of bottom pressure in granaries. The experiment demonstrates that the detection method for granary storage states based on a support vector machine (SVM) and domain knowledge shows high detection accuracy and low requirements for sensor performance and low detection cost. Therefore, it can meet the needs of remote online detection of granary storage states usually used.

摘要

粮仓状态的自动检测是确保国家粮食安全的一项重要技术。针对底压力的分布特征，本研究通过综合学习，非线性回归等技术从理论上探讨了谷底压力检测值与谷粒储藏状态之间的关系。在此基础上，建立了粮仓底部单环压力传感器的布置模型，并基于粮仓底部压力统计领域知识提出了粮仓储存状态的检测方法。实验表明，基于支持向量机和领域知识的粮仓状态检测方法，检测精度高，对传感器性能的要求低，检测成本低。