The invention relates to the technical field of
Internet of Things, and discloses an
Internet of Things-based campus self-service
laundry equipment abnormity
identification system, which comprises a user operation behavior alignment module, a
dynamic time warping algorithm quantification behavior deviation degree and a first
weight factor generation module, the multi-source state
feature extraction module is used for extracting an
equipment state matrix to generate a second
weight factor in combination with vibration spectrum and motor current
waveform analysis; the health degree fusion decision module dynamically distributes weights based on an entropy method, and calculates a health degree
score to trigger equipment-level
anomaly detection; the abnormal propagation topology modeling module constructs a fusion
topological graph, dynamically distributes propagation weights in combination with a historical
fault rate and a real-time load, and generates an abnormal propagation path probability graph; and the space-time early warning
visualization module integrates the equipment position and the abnormal probability, multiple alarm levels are divided, and the
risk distribution is dynamically displayed through the thermodynamic diagram layer, so that the detection precision and efficiency of abnormal identification of the campus self-service
laundry equipment are remarkably improved.