Design of Food Supply Chain Safety Early Warning Mechanism Integrating Big Data Analysis

The current food supply chain faces problems such as information fragmentation, difficulty in risk tracing, and delayed early warning, and it is urgent to establish an efficient early warning system. This paper aims to integrate multi-source heterogeneous data through big data technology to build a real-time risk prevention and control model. The method includes five steps: 1) IoT devices are deployed to collect full-chain data; 2) a Hadoop distributed storage platform is established, with an average daily data processing volume of more than 5TB; 3) a risk prediction model is trained based on the random forest algorithm, and feature engineering covers 50 + risk indicators; 4) dynamic early warning thresholds are set; 5) a visual early warning cockpit is developed and linked up with regulatory departments. Empirical results show that the model prediction recall rate reaches 96.2%; the early warning response time is shortened to within 30 min; the unqualified rate of pilot enterprises in random inspections drops to 5.7%. This mechanism significantly improves the risk prevention and control capabilities of the supply chain and provides technical support for government smart supervision.