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The safety of road infrastructure is the basis for safe operation of roads, and expressways, as high-grade expressways between freeways and non- freeways, involve roads, bridges, tunnels, traffic safety facilities and other infrastructure. Therefore, constructing a risk assessment index system for expressways is a prerequisite for the safety management of expressway operations, which helps to transform "passive processing" into "active prevention". In order to establish a comprehensive and systematic expressway risk assessment system, this paper from the perspective of systems engineering, uses the Interpretive Structural Modelling method to construct many risk factors of the expressway into a causal and hierarchical structure model. For solving the shortcomings of the poor accuracy of the subjective analysis method and the lack of interpretability of the objective analysis method, this paper adopts an improved subjective and objective analysis method to give weight to the indicators at various levels. So as to improve the scientificity of the risk assessment system of the expressway.

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During the process of grain storage, changes in seasons and weather will cause changes in the temperature and humidity of the granary, which will affect the occurrence of pests in the grain pile and the possibility of condensation, leading to problems such as grain deterioration and water loss. In this paper, based on the Bp neural network model, parameters such as temperature, humidity, moisture, warehouse temperature and warehouse moisture are used as input factors of the model to construct a multi-parameter grain analysis model. Through comparative analysis, the model optimization parameters are selected, and the trained neural network model is convenient for food administrators to obtain food storage status more intuitively.

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