This invention discloses a method for early warning and detection of biological particulate matter in air, belonging to the field of
environmental monitoring technology. It uses a high-speed, lensless
diffraction imaging
system to capture particulate matter
diffraction ring images in real time, extracts gray-level co-occurrence matrix texture features and
wavelet multi-scale features, fuses them to generate a real-time fusion vector, and constructs an index calculation model to calculate a biological index sequence. It identifies
biological attributes and uses multi-class SVM for classification, statistically represents concentration by the number of events, and combines Poisson
mutation detection to analyze the trend of change. When the concentration exceeds the standard or the trend is abnormal, it triggers a graded early warning. Through concentration adjustment feedback and multi-
source data verification, it identifies the type of misjudgment and dynamically optimizes the equipment, model, and threshold. This method achieves high-precision identification, stable monitoring, and intelligent early warning of biological particulate matter in the air, improving detection sensitivity and robustness, and reducing the misjudgment rate.