The invention discloses a
rehabilitation equipment fault
data monitoring method and
system, and particularly relates to the technical field of electric
digital data processing, comprising the following steps: S1, synchronously acquiring equipment
operation time sequence data and patient
interaction time sequence data, S2, performing
feature extraction and adaptive fusion on the two types of data, and S3, acquiring the data of the equipment
operation time sequence and the patient
interaction time sequence; s3, equipment health prediction and
rehabilitation curative effect deviation prediction are executed in parallel based on the fusion features, and the relevance of the equipment health prediction and the
rehabilitation curative effect deviation prediction is analyzed through a cross attention mechanism, and S4, a personalized dynamic
baseline model is combined, and an equipment fault risk value, a
curative effect deviation degree and a relevance confidence degree are integrated to generate hierarchical monitoring feedback. According to the invention, by constructing an equipment-patient-curative effect ternary closed-
loop analysis framework, the spanning from pure fault early warning to treatment effectiveness guarantee is realized, the problems of single monitoring dimension and unhooking with clinical curative effect in the prior art are solved, and the method has the outstanding effects of strong early warning perspectiveness, low
false alarm rate and support of
root cause analysis.