Method Of Aquatic Products Safety Assessment Under The Architecture Of Internet Of Things

Aquatic products get more and more people’s favor as a relatively high nutritional value of food. However, in recent years, aquatic products quality security incidents occur frequently, it not only brought great harm to people’s health, but also caused the interference to social stability. The methods of aquatic products quality safety assessment provides important early warning decision for the prevention of aquatic products quality safety incidents,which improves the quality of aquatic products and reduces the risk of consumers.However, in the method of the aquatic products quality safety assessment, the data may be lost or destroyed in the operation, and the evaluation index is bigger, it needs methods to fill the incomplete information table and attribute reduction based on rough set to complete the index data and simplify the evaluation index, making the assessment result of the aquatic products quality safety more real and accurate.The core content of this paper is the method of aquatic products quality safety assessment under the architecture of internet of things. Firstly, the thesis proposes the algorithm based on the tolerance of rough set to fill the lack of data of the aquatic products quality safety assessment. Then,using algorithm based on attribute unimportant to screen and simplify the large number data as assessment indicators after filling. Finally, puts the representative evaluation indicators into the BP neural network to train and predict,and realizes the evaluation of the aquatic products quality safety. In addition to, this paper researchs perception layer, transport layer and application layer of aquatic products quality safety assessment, and in order to build the aquatic product quality safety assessment overall architecture of IOT.