The invention relates to the technical field of medical monitoring, in particular to a multi-
source data integration grading early warning method and
system for
hand surgery vascular crisis. The invention discloses a multi-
source data integration grading early warning method for
hand surgery vascular crisis. The method comprises the following steps: S1, synchronously collecting
tissue oxygen saturation, an original and uninjured side
temperature difference value, behavioral environment data and clinical auxiliary data of a sentinel area through a multi-source sensor; s2, establishing a
behavior recognition algorithm model, recognizing interference behaviors of the patient in real
time based on the collected behavior environment data, and quantifying the interference behaviors to obtain influence data; and S3, based on the influence data, carrying out dynamic correction on the original and uninjured side
temperature difference value to obtain a corrected and uninjured side
temperature difference value, and carrying out dynamic correction on the
tissue oxygen saturation to obtain the corrected
tissue oxygen saturation. Through multi-
source data fusion and intelligent correction, interference of patient behaviors and environmental factors on
monitoring data can be effectively recognized and filtered out.