The present application relates to the technical field of grain
processing, and particularly relates to a grain
processing quality monitoring method based on multiple sensors, which comprises the following steps: whiteness monitoring;
abnormality index calculation;
abnormality type positioning; and sensitivity feedback. Through
multiple sensor cooperative monitoring and
statistical analysis, the present application realizes accurate identification and attribution of rice
processing quality abnormalities. By further introducing parameters such as equipment vibration,
object distance drift and particle count deviation rate, and combining historical data benchmarks for standardized processing, the
abnormality determination is more robust, and the true
process quality fluctuation caused by equipment vibration and
rice grain stacking can be effectively distinguished from the measurement
distortion, thereby avoiding false alarms caused by sensor interference. Through statistical observation of the occurrence frequency of various abnormalities within an
observation period, the tolerance of the whiteness threshold value is dynamically adjusted, and the problem of low accuracy of abnormality cause tracing and
slow response speed caused by the relative independence of various detection data is effectively solved.