| Summary: | Greenhouse is an important part of facility agriculture and a typical application scenario of modern agricultural technology. The greenhouse environment has the characteristics of nonlinearity, strong coupling, large inertia, and multiple disturbances. There are many environmental factors and it is a typical complex system [7]. In smart greenhouses, control commands are mostly triggered by complex events with multi-dimensional information. In this paper, by building the aggregation structure of complex events in the greenhouse, the technology is applied in the greenhouse as a whole. The core innovations of this paper are as follows: through the analysis of the information transmission process in the greenhouse, combined with the characteristics of the scene, a CEP information structure with predictive modules is formed, which is conducive to the popularization and application of CEP technology in the agricultural field. Pointed out the importance of extreme conditions in the prediction of the greenhouse environment for model evaluation. By improving the loss function in the machine learning algorithm, the prediction performance of a variety of algorithms under this condition has been improved. Applying CEP technology to intelligent greenhouse control scenarios, a set of practical complex event processing systems for greenhouse control has been formed.
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