The present application relates to the technical field of health monitoring, in particular to a
cancer survivor
work pressure monitoring system based on
big data, which comprises a physiological
parameter analysis module, a stress
cause analysis module, a work
load distribution module and an abnormal
stress monitoring module. In the present application, the sensitivity and accuracy of
data analysis are improved through the
difference analysis of
heart rate fluctuation,
skin conductance and
blood pressure range, the data points with large fluctuations are screened and classified, the efficiency of abnormal data capture and classification is optimized, the stress cause identification accuracy and
distribution law insight are improved, the key stress factors are determined through sensitivity sorting, the
stress evaluation is more scientific and quantitative, the priority dynamic adjustment is realized in load classification and grouping, the
work pressure distribution is optimized, the abnormal
stress point monitoring and grading judgment are strengthened, the stress
abnormality grade fine monitoring is realized, the data quantification and
dynamic monitoring capability are enhanced, the
stress management efficiency is improved, and the targeted data support is provided for personalized
health intervention.