This invention provides a
food inspection method, equipment, and medium based on industrial
big data. By deploying sensors on the
production line and the
enterprise information system, it collects and stores multi-source industrial
big data in real time. The collected multi-source industrial
big data is preprocessed, and a deep
autoencoder model is trained using historical normal batch datasets to determine the dynamic
reconstruction error threshold for
food quality characteristics. During online detection, the real-
time data stream is input into the deep
autoencoder model to calculate the
reconstruction error. If the error exceeds the threshold, an early warning is triggered, and the feature variable with the highest contribution rate is located. Using the located feature variable with the highest contribution rate, combined with association
rule mining, suspicious risk sources are output. This invention achieves reduced
false alarm rates and improved detection sensitivity by generating a dedicated dynamic threshold, locating abnormal contribution rates, and then combining association
rule mining to trace risk sources, thus achieving closed-loop real-time
quality control from early warning triggering to
risk source output.