Research on the architecture of food safety risk intelligence early warning system

2021 ◽  
pp. 1-11
Author(s):  
Yu Zhang ◽  
Yarui Zhang ◽  
Xiaocui Li

Food safety supervision involves all aspects of production, processing and sales. True, reliable and complete intelligence can realize the traceability of the entire process of food safety production, thereby ensuring that food safety incidents are controllable from the source. However, most studies only analyze the food safety risk identification and early warning from the perspective of information flow from the theoretical level, and lack specific applications at the practical level. Therefore, this study analyzes the system requirements and the overall business process of the system, expounds the goals and principles of system design, designs the overall framework of the system, and finally elaborates on the realization of its functions of the different functional modules of the system, so as to provide the early warning system development provides decision support and reference. Finally elaborates the realization of its functions according to the different functional modules of the system, so as to provide decision support and reference for the development of early warning system.

2016 ◽  
Vol 44 (12) ◽  
pp. 384-384
Author(s):  
Kristen Nelson McMillan ◽  
Kristen Brown ◽  
Charlotte Woods-Hill ◽  
Susan Floyd ◽  
Bonnie Staso ◽  
...  

2020 ◽  
Vol 8 (10) ◽  
pp. 5419-5425
Author(s):  
Ding‐Yan Lin ◽  
Cheng‐Han Tsai ◽  
Ying Huang ◽  
Siou‐Bang Ye ◽  
Che‐Hsuan Lin ◽  
...  

2021 ◽  
Vol 2021 ◽  
pp. 1-12
Author(s):  
Wei Tian ◽  
Jiang Meng ◽  
Xing-Ju Zhong ◽  
Xiao Tan

With the increasing exploitation and utilization of underground spaces, the excavation of deep foundation pits adjacent to existing metro tunnels is becoming increasingly common. These excavations have the potential to cause safety problems for the operation of the nearby metro. Therefore, to prevent metro tunnel accidents from occurring during the construction process and to ensure the safety of lives and property, it is necessary to establish a risk-based early warning system. During the excavation process, the main methods for preventing accidents in excavations adjacent to existing metro tunnels are manual analyses based on on-site monitoring data. However, these methods make it difficult to enact effective control measures in a timely manner owing to the lag of information processing. However, the trial application of artificial neural networks (ANNs) and building information modelling (BIM) for engineering projects provides a new method for solving such problems. This study uses a backpropagation neural network to predict the real-time deformation of the tunnel based on monitoring data from the adjacent construction site. A safety risk assessment model is then established based on the relevant specifications. Through the establishment of an intelligent warning system, the safety risk to the metro tunnel during the construction process can be displayed in a three-dimensional (3D) form using the BIM. The operation results of the ANN–BIM system show that it can effectively present the safety risk to existing metro tunnels in a 3D manner, which can provide managers with rapid and convenient visual information to inform their decision-making.


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