Design of FMCG Traceability and Consumer Information Big Data System based on Deep Learning

With the development of science and technology such as the Internet of Things, big data and artificial intelligence, the consumer market is gradually maturing, and the FMCG industry is developing rapidly. At the same time, the safety of FMCG is directly related to the life and health of the people. Therefore, the establishment of FMCG anti-counterfeiting and traceability system is urgent. Machine vision is used to collect the microscopic features of two-dimensional code (points, edges, lines, curves, etc.) and form the microscopic feature code. The feature points are extracted by Hessian algorithm, and then the extracted feature points are rotated, scaled, enhanced and restored by Fourier algorithm, etc. Establish a multi-level classification model of deep learning combined with support vector machine, and use convolutional structure neural network learning mode to achieve the ultimate recognition training of micro-features at all dimensional levels, realize new anti-counterfeit, and establish a big data system of FMCG traceability and consumption information based on deep learning, which can enhance consumers' trust in the quality and safety of FMCG and improve the added value of FMCG. Promote the transformation and upgrading of the entire industrial chain and consumption chain of FMCG, and promote the healthy and rapid development of the FMCG industry.