Consumer decision-making and smart logistics planning based on FPGA and convolutional neural network

In the fourth Industrial Revolution, cost-effective planning and rational management were the key to the success of the revolution. This paper mainly studies the development and application of models in machine learning technology. The abnormal activities monitored in real time are rectified so that the customer's electronic orders can be displayed through the support of big data, thus laying the foundation for the development of intelligent logistics. Under the data system, an exception model is created and classified and regressed. In this model, the security and stability of customer orders in the network can be automatically detected, and the abnormal data can be analyzed and evaluated. Unusual circumstances of this kind need to be in an intelligent logistics environment, and delivery tasks must be called intuitive for special care. Early detection of abnormal order events is expected to improve the accuracy of delivery planning. To enable new technical solutions, the logistics industry and economic decision-makers often lack the IT background and expertise needed to start developing new systems and technical solutions. Evaluate the benefits of using. Implementation and integration complexity is seen as one of the three major obstacles to the success of the IoT above. This is by hindering long-term investment in new technologies from slowing down digitization.