This study is based on the concept of converting agricultural waste into green new energy, we combine the concept of green cycle development and the relevant theories in modern system engineering to optimize the study of agricultural production waste recycling network. In this paper, the optimization of the agricultural production waste recycling network is divided into two aspects—facility site selection and vehicle path planning—with the objectives of agricultural production waste green recycling and the minimization of system construction and operational costs. In this study, the site selection and path planning problems were unified and an optimization model for the agricultural production waste recycling network site-path (LRP) problem was constructed. The optimization results of agricultural production waste recycling network facility location and recycling vehicle path planning were obtained by using the simulation data in the optimization model and designing the genetic algorithm design with the relevant characteristics of agricultural production waste recycling. The feasibility and operability of the model were verified through experiments. The research related to the optimization of agricultural production waste recycling networks can be used to both reduce production costs in agricultural areas and progress the practical theory of reverse logistics in agricultural areas. Agricultural waste resource utilization provides important support for the development of an ecological agriculture cycle and helps protect the environment.

This study takes a sample of green storage monitoring data for corn from a biochemical energy enterprise, based on the enterprise’s original storage monitoring system while establishing a “green fortress” intending to achieve green and sustainable grain storage. This paper proposes a set of processing algorithms for real-time flow data from the storage system based on cluster analysis to detect abnormal storage conditions, achieve the goal of green grain storage and maximize benefits for the enterprises. Firstly, data from the corn storage monitoring system and the current status of research on data processing algorithms are analyzed. Our study summarizes the processing of re-al-time stream data together with the characteristics of the monitoring system and discusses the application of clustering analysis algorithms. The study includes an in-depth study of the green storage monitoring system data for corn and the processing requirements for real-time stream data. As the main novelty of this research, the optimization algorithm model is applied to the green storage monitoring system for maize and is validated. Finally, the processing results for the green storage monitoring data for maize are presented in graphical and textual formats.

Through shipping service adaptability measurement, selecting shipping services that are more adaptable to preferences such as low cost, high efficiency, safety, and obvious emission reduction can achieve synergistic optimization of green shipping management. The study takes green shipping service adaptability as the research theme; explores three aspects, i.e., shipping safety, shipping rate and shipping choice preference, related to the evaluation and selection of a green shipping service; constructs the green shipping service adaptability evaluation index system including safety index, freight rate index and choice preference index; and applies fuzzy-exact by processing the historical data from H shipping company in Hainan Province, China. Bayesian net is applied to calculate the shipping safety adaptation degree of the transportation object. The theory of shipping service adaptability proposed in the paper can be applied to the fields of shipping supplier selection and shipping company’s detection of shipping object status. The fuzzy-exact Bayesian network method chosen in the paper can solve the problem of incomplete state coverage of the Bayesian network and correct the situation that some edge probabilities are unreasonable.