## 1 Introduction  
As technology develops, crop yields have greatly increased [1]. However, due to differences in geography and climate, crop production in some regions is far from meeting the demand. As yield cannot be significantly increased, it is crucial to effectively reduce grain consumption to preserve grain. The key to scientific grain preservation is \"temperature control\" [2], and the key to maintaining an appropriate temperature for grain is ventilation. Effective ventilation not only prolongs the storage time of grain but also maintains its quality. Nowadays, ventilation technology includes temperature-reducing ventilation, precipitation-reducing ventilation, anti-condensation ventilation, heat-dissipation ventilation, and conditioning ventilation [3]. Choosing the appropriate ventilation timing and completing ventilation control in different modes is the key to temperature control. However, currently, grain storage facilities can only detect \"three temperatures and two humidities\", which are the temperature of the grain pile, the temperature of the atmosphere in the granary, the temperature of the external atmosphere, the humidity of the atmosphere inside the granary, and the humidity of the external atmosphere. The commonly used ventilation control still relies on human judgment to start and stop ventilation equipment. Faced with the complex and ever-changing environment inside the granary, there is no scientific, accurate, or fast response strategy. The so-called multi-modal grain status refers to the state of different ventilation conditions under a global perspective, which is divided into temperature-reducing ventilation mode, precipitation-reducing ventilation mode, anti-condensation ventilation mode, heat dissipation ventilation mode, and conditioning ventilation mode. There has been a rapid development of deep learning and its applications in various fields, such as computer vision [4], speech recognition [5], natural language processing [6], medical diagnosis [7; 8], precision agriculture [9], stock market [10], and so on. As deep learning becomes more widespread, its applications in food security have also increased, but there have been relatively few practical applications in grain storage. Deep learning is a learning algorithm that uses multi-layer neural networks and has strong adaptability, robustness, and learning ability [11; 12]. The concept of multimodality originates at the intersection of cognitive science and computer science research. It refers to the acquisition of rich information through multiple sensory channels [13], such as vision, hearing, and touch, and the integration and joint processing of this information to obtain more accurate and comprehensive information and understanding. This multimodal information processing approach plays an important role in human cognition and communication [14] and has also become an important research direction in fields such as computer vision, speech recognition, and natural language processing. Based on the theory of computer multimodality, multi-modal grain storage refers to the classification of internal and external environmental conditions of a grain pile under ventilation into different modes. These modes include temperature-reducing ventilation, moisture-reducing ventilation, anti-condensation ventilation, heat dissipation ventilation, and conditioning ventilation. Multi-modal grain status control refers to the control method that changes a series of modes with the environmental changes of the grain pile. Therefore, combining deep learning with the latest multi-modal grain status control theory, researching the decision-making and control strategies of grain storage ventilation mode, achieving balanced grain temperature, preventing water condensation, stopping grain heating, reducing grain moisture, creating a low-temperature environment, improving grain storage performance, and reducing manual operation functions are of practical significance in ensuring grain storage safety [15].